Transport Demand Research Articles

An SD-LV Calculation Model for the Scale of the Urban Rail Transit Network

The planning for the scale of the urban rail transit network (URTN) is one of the key tasks of URTN planning. The scale should match the urban development (UD). A reasonable scale can improve travel efficiency, increase economic activities, and promote UD, while an unreasonable scale may consume more urban resources, fail to meet urban transportation demands, and even inhibit UD. Currently, the URTN scale is primarily determined by qualitative analyses and static indicators, which leads to the scale does not match UD perfectly. To determine a reasonable scale, a System Dynamics–Lotka–Volterra (SD-LV) model is constructed. The SD model is adopted to simulate the dynamic interaction between the URT and UD. The LV (Lotka–Volterra) model is employed to calculate the scale, in which the mutualism coefficients are proposed to characterize the mutualistic relationships between the URT and UD. The model is validated by using a dataset of the Beijing URTN from 2017 to 2021. The simulation errors of the URTN scale range from −4.3% to 1.32%, which demonstrates the robustness and effectiveness of the proposed model. The study offers quantitative theoretical insights for determining the reasonable scale of the URTN.

Fleet Repositioning, Flag Switching, Transportation Scheduling, and Speed Optimization for Tanker Shipping Firms

In response to the European Union (EU)’s sanctions on Russian oil products, tanker shipping firms may adopt two strategies to reoptimize their shipping networks. The first strategy is to switch the flag states of tankers that are not eligible to operate on certain routes. The second strategy is to reposition tankers based on their flag states, i.e., moving those tankers that are eligible from other groups to specified routes. To help tanker shipping firms minimize the total operating cost during the planning horizon in the context of EU oil sanctions, including costs of fleet repositioning, flag switching, and fuel, this study investigates an integrated problem of fleet repositioning, flag switching, transportation scheduling, and speed optimization considering the dynamic relationships among fuel consumption, speed, and load. By formulating the problem as a nonlinear integer programming model and applying various linearization techniques to convert the nonlinear model into a linear optimization model solvable by off-the-shelf linear optimization solvers, this study demonstrates the practical application potential of the proposed model, with the longest solution time of less than two hours for a numerical instance with seven routes. Furthermore, through sensitivity analyses on important factors including unit fuel prices, crude oil transportation demand, and the tanker repositioning cost, this study provides managerial insights into the operations management of tanker shipping firms.

How to apply the four-step model for 150,000 travel zones: The HIPAT model

Current European transport demand models following the classical four-step approach are less suitable for analysing the traffic volumes on inter-regional road infrastructure with relevant demand of regional trips, as they consider only about 10% of the trip demand with passenger cars. Mainly due to runtime problems, these models are limited to operation at the NUTS-3 level. This level comprises around 1500 regions for Europe with an average diameter of about 50km. However, more than 90% of the trips are shorter than 50km and are, therefore, not adequately considered in the trip distribution model and the trip matrix. Computing this matrix at a higher spatial resolution, such as the LAU-2 level, is not possible as the runtime of the overall model and the size of the trip matrix increase quadratically with the number of travel zones. In the HIPAT model, this obstacle is overcome by an innovative concept operating at different hierarchical levels. This reduces the complexity of the trip matrix, such that the HIPAT model can be applied at large scale for 150,000 travel zones without any runtime problems. This is demonstrated by a prototype implementation for a case study that handles 33,000 travel zones in two minutes. In addition, the limitations of applying NUTS-3-based travel zones for transport modelling are shown: 94.6% of the trips are shorter than the average diameter of the travel zones (50km) and are therefore not correctly represented by the model. Smaller travel zones decrease this number and provide a better basis for analysing infrastructure policies and, ultimately, for decision-making.

Evaluating pedestrian facilities for enhancing pedestrian safety in Addis Ababa city

Transport demand has been increasing in most third-world countries. However, poorly maintained facilities and poor planning exposed pedestrians to high-traffic accidents. This paper aims to evaluate pedestrian facilities using the pedestrian level of service to enhance pedestrian safety in Addis Ababa. Relevant data was collected through field measurement and direct observation from 13 purposively identified pedestrian sites across the Piyasa to Bole bridge and Megenagna to Torihayloch road corridors. Pedestrian level of service derivation considered capacity, traffic, comfort, safety, and security conditions and their sub-components. The weight of each criterion and sub-factor was decided according to the Saaty scale using the judgment of experts under the analytical hierarchical process. The result of the study strongly stipulates the presence of the problem of pedestrian facilities in Megenagna, Mexico, Urael Church, and Golagol which can affect the level of pedestrian safety. However, Denibel City, Legahare, Torhayloch, and National Theater sites have scored better Pedestrian level of service for the walking population. Finally, the study recommended the requirement of prior attention for the sites with very poor and poor pedestrian levels of service by the concerned sectors.

Transforming Cities Through Renewable Energy: Analyzing the Role of Green Infrastructure in Driving Urban Economic Growth

Cities worldwide are experiencing rapid population growth, leading to increased demand for energy, transportation, and other infrastructure. This poses significant challenges in providing sustainable and environmentally friendly energy. This research aims to analyze the role of green infrastructure in driving urban economic growth through the implementation of renewable energy. The method used in this study is a literature review with a qualitative approach. Data were collected from articles published on Google Scholar between 2013 and 2024. The study results indicate that the use of renewable energy and the development of green infrastructure play a crucial role in transforming cities towards sustainability. The main findings show that renewable energy has a positive impact on reducing carbon emissions, while green infrastructure improves the quality of life and drives urban economic growth. Cities in Indonesia, such as Bandung, Surabaya, and Jakarta, have provided tangible examples of success in implementing these strategies.

Enhancing Transportation Efficiency with Interval-Valued Fermatean Neutrosophic Numbers: A Multi-Item Optimization Approach

In this study, we derive a simple transportation scheme by post-optimizing the costs of a modified problem. The strategy attempts to make the original (mainly feasible) option more practicable by adjusting the building components’ costs. Next, we employ the previously mentioned cell or area cost operators to gradually restore the modified costs to their initial levels, while simultaneously implementing the necessary adjustments to the “optimal” solution. This work presents a multi-goal, multi-item substantial transportation problem with interval-valued fuzzy variables, such as transportation costs, supplies, and demands, as parameters to maintain the transportation cost. This research addresses two circumstances where task ambiguity may occur: the interval solids transportation problem and the fuzzy substantial transportation issue. In the first scenario, we express data problems as intervals instead of exact values using an interval-valued fermatean neutrosophic number; in the second case, the information is not entirely obvious. We address both models when uncertainty solely affects the constraint set. For the interval scenario, we define an additional problem to solve. Our existing efficient systems have dependable transportation, so they are also capable of handling this new problem. In the fuzzy case, a parametric technique generates a fuzzy solution to the preceding problem. Since transportation costs have a direct impact on market prices, lowering them is the primary goal. Using parametric analysis, we provide optimal parameterization solutions for complementary situations. We provide a recommended algorithm for determining the stability set. In conclusion, we offer a sensitivity analysis and a numerical example of the transportation problem involving both balanced and imbalanced loads.

Public Transportation System and Sustainable Development of Urban Transportation under Transit Oriented Development Mode: A Case Study of Jinan City

For more than 40 years since the reform and opening up, Chinese cities have faced tremendous pressure on transportation. In recent years, the Chinese government has recognized the importance of developing public transportation. From easing traffic congestion, improving air quality, and increasing the happiness index of residents, the government is making progressive efforts so as to upgrade sustainable urban development. The Transit Oriented Development (TOD) mode of public transportation is adopted to provide ideas for the sustainable development of urban transportation. This research analyses the theoretical mode of sustainable development of public transportation TOD, takes economy, environment and society as the entry points, and then analyzes the current situation of TOD mode development of public transportation in Jinan. After summarizing the shortcomings of its public transportation master plan and combining the excellent cases of Tokyo, Japan, and Hong Kong, China, this study analyzed the advantages of successful public transportation TOD mode for Jinan’s reference. Secondly, based on the basic transportation situation of Jinan, the future sustainable development of TOD mode is elaborated. In terms of transportation supply, transportation demand, and transportation system, it puts forward suggestions for sustainable development, including “congestion tax”, “rail + property” linked development, and rational planning of the urban layout. It also looks forward to the future development of Jinan in terms of economic, environmental and social aspects of sustainable development, in order to help Jinan improve the TOD mode to overcome the traffic congestion problem and promote the sustainable development of the city.

Application of Improved Genetic Algorithm and Deep Learning in Cold Chain Logistics Distribution Demand Prediction

In order to solve the problem of inaccurate prediction results caused by the excessive impact of downstream on upstream suppliers in the process of cold chain logistics transportation demand prediction, the author proposes a demand prediction system Multi agent based on improved genetic algorithm and deep learning. The system will improve genetic algorithm, combine deep learning with practical problems in cold chain logistics supply chain, and evaluate the improved model through instance simulation. The results are as follows: After optimization, the order quantity of each stratum reduces the influence of retailers on upstream suppliers by more than 70%. As for the overall transportation cost, it shows a continuous upward trend within 20 cycles, while the total cost of each cycle fluctuates in a lower range after optimization, reducing the overall total cost by about 50%. It shows the reliability of the improved demand forecasting system in this study to greatly reduce storage and transportation costs.

Feasibility Analysis of Solar Energy and Role of Lithium-Ion Battery Reserved in Electric Vehicle Market: A Path Towards Green Transportation

Solar-based vehicular electrification is bound to play a vital role in Green House Gas (GHG) emission reduction from conventional transports and supporting the fight against environmental degradation. This paper presents the feasibility of solar-based electric vehicle charging (SEVC) in India, a shift of the transportation sector to renewable energy implementation. Solar-electric vehicles may transform the scenario in the coming future in India on a larger scale. Extensive negative environmental impact is produced by transport activities as it pours out 14% of global GHG emissions. The transportation sector is the second major contributor to pollution in India. Currently, dealers of non-renewable resources like coal, petroleum, etc. are facing a tough time meeting increasing demand. This results in limited resources which are being suppressed by the dealers and producers to meet this need which leads us to an insecure future, having scarcity of minerals and fuel. In this paper, we perform a graphical analysis of fuel consumption in India, the growing demand for green transportation in the Indian market and solar potential in our country. In Feb 2023, India’s government revealed the finding of a 5.9 million tons stockpile of unexplored lithium deposits in the Jammu and Kashmir state. So, the price of batteries would be significantly impacted by this, making EVs more accessible to customers. This paper also includes challenges in adopting solar-based transportation, steps of the Indian Government towards green transportation and recommended policies.

TURİZM LOGİSTİKASI VӘ ONUN MӘHSULU OLAN NӘQLİYYAT TӘŞKİLEDİCİSİNİN ARAŞDIRILMASI

In the article, taking into account that the provision of transport is one of the main elements of the tourism potential and also creates an opportunity for the economic development of the regions, it is possible to determine the demand for transport in tourism and also to increase the competitiveness of tourism by deeply investigating this activity of the tourism product as a transport organizer in the mentioned directions. The article also deeply examines the possibilities of magnetoplanes as a productive and efficient means of passenger transport, taking into account that they are considered as fast as airplanes, as reliable as railway transport, as convenient as cars, and that they have already won the favor of passengers and tourists in a number of developed countries. the plan and prospects of its application as a tourist vehicle are mentioned.

Modelling a DC Electric Railway System and Determining the Optimal Location of Wayside Energy Storage Systems for Enhancing Energy Efficiency and Energy Management

Global demand for fossil fuels is highly increasing, necessitating energy efficiency to be enhanced in transitioning to low-carbon energy systems. Electric railways are highly efficient in reducing the transportation demand for fossil fuels as they are lightweight and their energy demand can be fed by renewable energy resources. Further, the regenerative braking energy of decelerating trains can be fed to accelerating trains and stored in onboard energy storage systems (ESSs) and stationary ESSs. It is fundamental to model electric railways accurately before investigating approaches to enhancing their energy efficiency. However, electric railways are challenging to model as they are nonlinear, resulting from the rectifier substations, overvoltage protection circuits, and the unpredictability and uncertainty of the load according to the train position. There have been few studies that have examined the ESS location’s impact on improving the energy efficiency of electric railways while using specialised simulation tools in electric railways. However, no single study exists that has studied the location impact of stationary ESSs on the energy efficiency of electric railways while the trains are supported by onboard ESSs. Given these goals and challenges, the main objective of this work is to develop a model using commercial software used by industry practitioners. Further, the energy saving is aimed to be maximised using stationary ESSs installed in optimal locations while trains are supported by onboard ESSs. The model includes trains, onboard ESSs, rail tracks, passenger stations, stationary ESSs, and traction power systems involving power lines, connectors, switches, sectioning, and isolators. In this article, a test scenario is presented comprising two trains running on a 20 km with three passenger stations and two substations. The trains and track are modelled in OpenTrack simulation software (Version 1.9) while the power system is modelled in OpenPowerNet simulation software (Version 1.11). The two simulation tools are used in the railway industry and can produce realistic results by taking into account the entire electrical network structure. A stationary ESS is added on the wayside and moved in steps of 1 km to obtain the optimal location before investigating the impact of stationary ESSs on the performance and energy management of onboard ESSs. It is found that the energy saving when installing a stationary ESS at the optimal location is 56.05%, the peak-power reduction of Substation 1 is 4.37%, and the peak-power reduction of Substation 2 is 18.67%.

Role of Artificial Intelligence in Diabetic Wound Screening and Early Detection

: The morbidity and death rates linked to diabetes mellitus are substantially increased by foot ulcers, a prevalent consequence of the disease. Proper wound management is essential for controlling foot ulcers. This includes monitoring the ulcers' healing progress through clinical reviews, changing dressings as needed, treating infections with the right medications, and ensuring that the ulcer is offloaded correctly. Taking pictures of the ulcer was a dependable way to track how diabetic foot ulcers were healing in the past. Images of foot ulcers have recently experienced a tremendous change due to the emergence of digital cameras in cell phones. Artificial intelligence (AI) and other recent developments in digital health technology present a great chance to improve the efficiency of diabetes care, which might reduce the growth in healthcare costs associated with diabetes. Patients with diabetes can alleviate the burden on clinics and patients' transportation demands by electronically sharing photos of their ulcers, which diabetes care providers can remotely monitor. Improved remote monitoring of diabetic foot ulcers using smartphone apps is now possible with the help of a new generation of AI-powered solutions. This clinical update review aims to gather information on this trending topic so that medical professionals can be current on all the latest advancements in the field.

Solution approaches to reduce problems with unbalanced supply and demand in transportation and harvest planning

ABSTRACT This study addresses forestry planning challenges arising from supply-demand imbalances. In forest planning, supply often exceeds demand because supplies are known in advance, while demands are known more short term when ordered. This leads to so-called “creaming,” where forest planners select nearby areas first. With static supply and incremental demand information, average transportation distance increases over the planning horizon. To mitigate this, we propose an approach to artificially balance supply and demand. This can be achieved by including additional time periods with additional demand making up the factual difference. We evaluate three planning approaches to model the extended demand, varying the number of time periods and extension duration. Through simulations, we compare these approaches to traditional methods and theoretical solutions. Our proposed approach aims to better keep the average distance balanced throughout the overall planning periods. It ensures that average transportation distances are not excessively favorable in the initial periods, nor unreasonably high in the later periods, resulting in a favorable equilibrium in the average transportation distance over time. It makes sure that we do not need the additional truck capacity at certain times. We assess our proposed approaches using a case study from a Swedish forestry company, demonstrating their superiority over current practices.

A Case Study of Existing National Highway no. 5 & 6

Abstract: The case study of National Highways 5 and 6 provides insights into the infrastructure development, economic impact, and challenges associated with these critical transportation corridors. National Highway 5 stretches from Chennai to Kolkata, while National Highway 6 runs from Mumbai to Kolkata, serving as vital arteries connecting major cities and facilitating trade and commerce across regions. Both highways underwent significant upgrades and expansions to meet the growing demands of transportation and enhance connectivity between key economic hubs. This involved road widening, modernization of toll systems, and the implementation of safety measures to improve the overall quality of the highways. The case study may conclude by discussing future prospects for NH 5 and 6, including potential expansions, technology integration for smart transportation systems, and strategies to address emerging challenges such as climate change resilience and sustainable mobility.

Optimization and analysis of chassis for heavy electric truck under different condition

The design of heavy electric truck chassis is a critical aspect of developing sustainable and efficient transportation solutions. Heavy electric trucks, which are powered by electric motors and rely on large battery packs for energy storage. the structural integrity and strength of the chassis are of utmost importance. the integration of the battery pack into the chassis is a critical consideration. The chassis needs to accommodate the size, weight, and distribution of the battery pack while ensuring proper thermal management and protection. the unique challenges posed by these vehicles. By considering factors such as structural integrity, battery integration, lightweight materials, and other relevant design considerations, it is possible to develop efficient and sustainable heavy electric trucks that meet the demands of modern transportation.

Transfer Learning for Transportation Demand Resilience Pattern Prediction Using Floating Car Data

Understanding the response of a transportation system to disruptive events is significant for evaluating the resilience of the system. However, data collection during such events is always challenging, and the data volume is insufficient for building a robust model. Transfer learning provides an effective solution to this problem. In this study, we propose a floating car data (FCD) driven transfer learning framework for predicting the resilience of target transportation systems to similar disruptive events to those that have ever occurred in the source systems. The core of the framework is an unsupervised pattern extractor that combines the k-Shape clustering and Bayes inference methods for extracting resilience patterns from the FCD collected in the source systems during the disruption period. The extracted patterns can then be used to assist in the prediction of the resilience of the target systems. We examine the effectiveness of the proposed framework by conducting a case study under the context of the COVID-19 pandemic, in which the source domain cities include Antwerp and Bangkok, and the target domain city is Barcelona. Results show that the extracted resilience patterns can improve the prediction performance of transfer learning neural networks with less pre-event information and limited data volume.

Road Network Traffic Flow Prediction Method Based on Graph Attention Networks

With the acceleration of urbanization and the continuous growth of transportation demand, the traffic management of smart city road networks has become increasingly complex and critical. Traffic flow prediction, as an important component of smart transportation systems, is of great significance for optimizing traffic planning and improving traffic efficiency. The study collected and preprocessed traffic data in the smart city road network, including multi-dimensional information such as traffic flow, road conditions, and meteorological data. Then, based on the idea of graph neural networks, we constructed the topological structure of the urban road network and abstracted elements such as roads and intersections into nodes, using edges to represent their connection relationships, thus forming a graph dataset. Next, we introduced an attention mechanism to extract more representative node features through the weighted aggregation of node features, thereby achieving effective modeling of urban road network traffic flow. During the model training phase, we used real traffic datasets for experimental verification and integrated various information such as time, space, and road features into the model. The experimental results show that compared to traditional methods, this research prediction method has achieved better performance in traffic flow prediction tasks, with higher prediction accuracy and robustness. It has stronger applicability and effectiveness in different traffic scenarios. By integrating multi-dimensional information and introducing attention mechanisms, this method has significant advantages in improving the accuracy and robustness of traffic flow prediction, and has important practical significance and application prospects for the construction of smart transportation systems and the development of smart cities.

ВЫЯВЛЕНИЕ ПОТЕНЦИАЛЬНЫХ ГОРОДСКИХ ЦЕНТРОВ НА ОСНОВЕ МОДЕЛИРОВАНИЯ ПАССАЖИРОПОТОКОВ (НА ПРИМЕРЕ САНКТ-ПЕТЕРБУРГА)

The article considers the development of methodological foundations and suggests methodological approaches to identify urban areas with signs of subcenters in the implementation of a polycentric model of urban development. It is demonstrated that the application of mathematical modeling of passenger flows contributes to solving the problem of urban areas differentiation according to criteria of transport accessibility, time costs and travel speeds. As part of the study, calculations of passenger flows and passenger turnover were performed on the example of the St. Petersburg agglomeration – using network and pre-network models. Comparing the values of transport demand and transport supply in the nodes of the network made it possible to identify the location of potential urban centers.

Research on Public-Private Partnership (PPP) Investment under Build - Operate - Transfer (BOT) and Build-Lease-Transfer (BLT) Contract Types in the High-Speed North-South Rail Project (HSR), the Hanoi - Vinh Line

Introduction The North-South high-speed rail development is a completely new field in Vietnam. Calculations show that Vietnam's high-speed rail system will attract a significant amount of investment, which is 4-5 times higher than developing highways, and the annual operation and maintenance costs are three times higher. Methods Public-Private Partnership (PPP) investment is effective and required to ease the strain on the state budget. This research aims to build a database for calculating the financial efficiency of the high-speed rail project (Hanoi -Vinh line), thereby establishing a mechanism for sharing benefits and risks between the state and investors through financial scenarios: applying Build - Operate - Transfer (BOT) contracts with state participation rates of 50% and 80%, respectively. Results The qualitative research method is used to determine the indicators influencing the project's benefits and costs, assume and adjust economic, financial, and fare parameters based on Vietnam's actual conditions, and investigate and survey the affected areas to assess passenger transport demand. Conclusion Furthermore, the financial efficiency of the project is calculated, and various participation options for the parties involved are proposed to attract investors.

Simulation for Designing the Transition to Autonomous Shipping – Japanese Coastal Shipping

In Japanese coastal shipping, there is a need to introduce automation technology to alleviate the shortage of seafarers, but its introduction has an indirect impact on coastal shipping due to the interaction between transportation demand and freight rates in a market with a variety of stakeholders. Therefore, it is difficult to make decisions about its introduction. This study uses a simulator that mimics the Japanese cargo market to evaluate the impact of the deployment. The results show that the introduction of autonomous vessels, even in the middle of development, may bring benefits, and that remote maneuvering technology with a crew on board may not produce positive impacts.