Transport Demand Research Articles

Transport challenges in Brazil and research opportunities

Located in South America, Brazil has continental dimensions and a population of approximately 203 million. As expected, the country has many mobility challenges and issues, as well as various research gaps and opportunities to promote improvements in urban transportation planning. This article aims to present some of these mobility challenges, such as the lack of transportation surveys and data in many Brazilian cities, decreased demand for public transport, increased demand for ridesourcing, telecommuting, COVID-19, and freight transport. We also mentioned the National Association for Transportation Research and Teaching (ANPET in Portuguese) and its important role in promoting annual conferences and supporting transportation research and education in the country. After making some critical reflections, we have outlined various research challenges that aim to improve the future of mobility in Brazil.

How does the built environment affect intermodal transit demand across different spatiotemporal contexts?

Bus and metro are the two primary modes of public transportation in many megacities worldwide. Understanding their cooperation is crucial for the integration of the public transportation system. Despite extensive research on public transportation demand, studies focusing on bus-metro cooperation remain limited. Intermodal transit demand directly reflects the level of cooperation between the two modes in travel behavior. In this study, intermodal transit demand is extracted from smart card data in Beijing, China. The extreme gradient boosting algorithm is employed to investigate the determinants of intermodal transit demand considering spatiotemporal variation. The SHapley Additive exPlanations method further interprets these models. Findings reveal that (1) the relative spatial relationship between bus and metro service facilities significantly influences their cooperation; however, these influences gradually weaken as urban space expands from the core to the peripheral area; (2) in peripheral area, the characteristics of the bus network hold the highest average importance ranking; (3) extensive nonlinear relationships and threshold effects exist between the built environment and intermodal transit demand, with the magnitude, pattern, and direction of these impacts varying significantly across different spatiotemporal contexts; and (4) changes in the spatial layout of transportation service supplies impact their competition and cooperation, such as adequate bus service supplies potentially reducing the cooperation between bus and metro to some extent. These findings will assist planners and public transit operators in developing regulations that encourage cooperation between bus and metro, thereby increasing the attraction and competitiveness of the public transit system.

Corona crisis disruptions and consequences for demand for freight transport: A screening of the effects for the largest logistics service providers in Norway

AbstractThe present article investigates corona crisis disruptions and consequences for freight transport demand, using shipment data from major logistic service providers in Norway, as well as interviews. We investigate how Covid-19 affected demand for freight transport and transport volumes in different segments, changes for parcel delivery, home-deliveries, which industries were most affected and discussed main issues for LSPs in maintaining robust transport chains. We find temporary reductions in B2B deliveries after the first Norwegian societal lockdown but large volume increases in B2C deliveries, which remained at higher levels and saw further waves of increases. Especially home deliveries and mailbox deliveries of parcels increased, particularly in relation with new infection waves. At an aggregate level, retail trade experienced by far the largest volume increases, followed by transport and storage. The latter can in part be due to increasing low-volume import (parcels) from abroad being attributed to e.g. warehouses or customs. At the other end of the scale, wholesale trade stands out with the largest tonnage decrease, followed by manufacturing.

Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems

This research explores advanced computational intelligence techniques aimed at enhancing real-time decision-making in autonomous systems. The increasing reliance on autonomous technologies across sectors such as transportation, healthcare, and industrial automation demands robust, adaptive, and reliable decision-making frameworks. This study introduces a novel hybrid model that integrates Reinforcement Learning (RL), Deep Neural Networks (DNN), and Fuzzy Logic to enable autonomous systems to make accurate and timely decisions in complex, dynamic environments. The proposed framework leverages RL for adaptive decision-making, DNNs for pattern recognition and prediction, and Fuzzy Logic for handling uncertainty in system states. Experimental evaluations were conducted using high-fidelity simulations across three scenarios: autonomous vehicle navigation, real-time patient monitoring in healthcare, and robotic process automation. Results indicate a 25% improvement in decision accuracy, a 30% reduction in response time, and enhanced robustness against environmental variability compared to conventional decision-making methods. The findings underscore the effectiveness of computational intelligence in supporting critical decisions in real-time, marking a significant step toward more capable and reliable autonomous systems.

Roadmap for implementing climate adaptation innovations in railway

AbstractThe railway is often perceived as an industry where new technology is not utilised to its full potential. However, the future of the railway and its ability to respond to future transportation demands lies in its ability to implement emerging technology. The aim of this research was to propose a system-level innovation roadmap for implementing innovations that can support climate adaptation of railway infrastructure. To achieve the aim of the research, barriers, and enablers for implementing innovations have been explored, identified, and analysed. Two interview studies and a workshop have been conducted. In total, 58 experts from a population with knowledge in railway have been interviewed. The innovation is a decision support system (DSS) enhancing knowledge regarding effects of climate change on railway infrastructure and suggesting climate adaptation measures. The barriers and enablers concerning the implementation of the innovation are related to technology, business models, procurement, infrastructure, regulations, policy, and culture, values, and behaviour. Procurement processes and business models are vital enablers for the introduction of new solutions for a more climate-change-resilient infrastructure. Tendering documentation must include a broader scope of climate-change risk management indicators. A conclusion is that climate adaptation action has a multi-dimension aspect which require various stakeholders to be engaged. This work proposed a holistic system-level innovation roadmap for implementing DSS for climate adaptation of railway infrastructure.

A study of short-term passenger flow forecasting for public transportation based on the time series method

Abstract. In order to solve the conflicts between the supply and demand of urban transport, improve the living standard of urban residents, raise the level of urbanization, and promote the sustainable socio-economic development of the whole city, it is necessary to increase the pace of urban transport construction, planning and management in China. And at the same time, its necessary to take various measures to regulate the structure of urban transport and guide urban transport to develop in the direction of taking public transport as the mainstay. The experience of the development of urban transportation in China and abroad also proves that prioritizing the development of public transportation is the fundamental way to solve the problem of urban transportation. Public transport passenger flow data is the basis for optimizing public transport operations and scheduling. Accurate passenger flow forecasts on public transport routes can effectively guide public transport operation decisions, formulate operation scheduling plans and effectively improve the operational efficiency of the public transport system. The following are some of the key factors that can be taken into consideration when making passenger flow forecasts. The main contribution of this article is to propose the use of data mining technology, use bus IC card data and bus GPS data to integrate and organize them, conduct correlation analysis on the data, and on this basis, obtain the main distribution characteristics of bus passenger flow, and provide short-term Passenger flow forecast provides relevant ideas.

A model for container stowage planning considering stability constraints

The growth in global trade has led to the deployment of mega-container ships to meet increasing transportation demands. However, the larger vessel sizes pose significant challenges for planners in arranging container loading. This study addresses the container stowage planning problem for near-sea routes, incorporating stability constraints, container types, and technical limitations related to stack weight and stress. We propose a mixed integer linear programming model to describe this problem and generate load plans that minimize total costs associated with re-stowing and bending moments (BM). To solve the model efficiently, we design a dynamic weight and time-varying particle swarm optimization (PSO) algorithm, which combines particle classification and time-varying acceleration constants. The effectiveness of the model and the efficiency of the algorithm are validated through extensive numerical experiments. The results demonstrate that our proposed algorithm outperforms the traditional PSO algorithm in both solution quality and computation time. Additionally, sensitivity analyses are conducted to derive potentially useful managerial insights for supporting shipping liners’ decision-making. This study highlights the benefits of particle classification and time-varying acceleration constants, and reveals the influence of container bay capacity and permitted frame BM.

Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion

The growth of urbanization, population, and economic activity has led to a substantial increase in freight transportation demand, exceeding the capacity of existing infrastructure and creating new challenges across various regions. This has resulted in significant traffic congestion, increased travel times, and higher operational costs for commercial vehicle fleets. Leveraging topological data, such as road networks and traffic patterns, can enable more efficient routing strategies to navigate around congested areas. This study presents a comprehensive approach to truck rerouting strategy by integrating spatial analysis, truck characteristics, traffic conditions, road geometry, and cost–benefit analysis to select alternative routes suitable for commercial vehicle fleets. Incorporating real-time traffic information and predictive analytics, commercial vehicle operators can optimize their routes, reduce fuel consumption, and improve overall delivery efficiency. Three case studies were presented to demonstrate the proposed diversion decision framework. Two scenarios were designed for each case study: a base scenario with no diversion and an optimized scenario with a diversion strategy. The travel times, fuel consumption, and economic impacts between the two scenarios were compared and quantified as a total annual saving of USD 52 million. This approach goes beyond selecting alternative routes and provides decision makers with measurable benefits that justify diversion strategies.

Inter-regional rail travel and housing markets connectedness between London and other regions

Taking London as a location with which to measure the ripple effect in the UK housing market, this study aims to explain and verify the high degree of correlation between inter-regional transportation and the regional correlation of the housing market. Based on the literature on the relationship between short-term mobility and long-term migration, this paper illustrates that the extent to which people use trains for travel across regions will be related to the ripple effect in the regional housing markets. Frequent railway transport behavior, whether for commuting or traveling, might increase people's desire to relocate between regions, and thus leading to information transmission effects across regional housing prices and transaction volume. First, we estimate a dynamic indicator for the ripple effect. Then, the empirical tests use panel data, including the ripple indicator and passenger number data across time (1996–2022) and regions (nine regions). It is found that if London house prices drive other regional house prices to rise, inter-regional transportation demand will increase, and in turn, the increase of house prices in other housing markets will again drive up London house prices. The number of passengers will affect the information transmitted by the housing market transaction volume in other regions to the London housing market. This implies that higher inter-regional transport needs may lead to migration between London and other property markets, causing their transaction volumes to change in the same direction. The results of this paper verify that travel behavior between regions is a crucial factor in the leading/lagging behavior of regional housing market performance, implying a relationship between short-term travel and long-term migration. The results also indicate that incorporating variables of housing market correlations may help in the prediction of passenger numbers or transportation demand.

Assessing the environmental benefits of passenger cars electrification in metropolises: A case study of Singapore

Land-based transportation is a significant contributor to global greenhouse gas (GHG) emissions, with internal combustion engine vehicles being the predominant mode of transport in this sector. As the world population grows and urbanization accelerates, transport demand in metropolitan areas is projected to double by 2050, underscoring the critical need for sustainable transportation strategies, forecasting, and planning within an urban context. One challenge in this endeavor is the insufficient attention given to the distribution of vehicle grades within the overall car population during GHG impact assessment, despite the recognized importance of car grade by researchers and policymakers. In this study, we incorporate both engine type and vehicle grade distribution, derived from data on over 30,000 passenger cars registered with the Land Transport Authority of Singapore between 2018 and 2022, into our life cycle assessment analysis. This approach ensures that our scenario-based studies on prediction and planning are more comprehensive than those that consider only the most popular models. Furthermore, while Singapore benefits from a relatively clean electricity supply, the current limitations in charging infrastructure negatively affect the GHG impact of electric vehicles. The insights from this research will inform the development of transportation policies for metropolises like Singapore, contributing to the advancement of a more sustainable transportation system.

An integrated policy approach for sustainable decarbonization pathways of energy system in a city under climate change scenarios

An integrated assessment model has been developed to estimate useful energy demand, followed by an optimization model for planning the energy supply. Four scenarios have been developed. The findings of this study highlight the impact of behavioral, climate change, and socio-economic factors on energy demand and supply. Specifically, these factors can lead to significant variations in demand, such as a more than 3.48-fold difference in cooling demand or a 1.75-fold difference in the transportation demand by cars across various scenarios.The use of a dual-fuel car and electric motorcycle is suggested in all scenarios. Waste processing has been done with a landfill in all scenarios except in the final years of the net-zero emission target scenario, where an incinerator is proposed. The supply model for electricity generation in the early years suggests the combined cycle power plant followed by solar energy at the end. However, in the low-cost electricity scenario, it is always suggested to import electricity. The sensitivity analysis showed that the emission penalty and electricity price are the most important parameters. The results of this study will help policymakers and decision-makers in energy system planning and provide researchers with appropriate information, tools, and methods for future research.

Classification of Faults in Power System Transmission Lines Using Deep Learning Methods with Real, Synthetic, and Public Datasets

Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system’s protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems.

Shared Mobility's Role in Sustainable Mobility: Past, Present, and Future

This article reviews shared mobility, a prominent urban transportation concept with considerable potential to contribute to more sustainable urban mobility. Shared passenger mobility spans diverse services, often leveraging technological advances and disruptions such as smartphones and data analytics to optimize transport resources. Given the broad range of services, a shared mobility taxonomy is proposed, accommodating evolving services. Key challenges for delivering efficient and effective shared mobility services with lower environmental impacts are also identified. Finally, the International Transport Forum transport demand models are used to analyze policy implications and potential effects quantitatively. This article presents a possible scenario for the global evolution of these services to 2050. Results emphasize shared mobility's role in transport decarbonization in the present and future and show that shared mobility may reduce resource use and mobility externalities (e.g., CO2, local pollutants, congestion, urban space use) but that the uptake will differ between Global South and Global North cities.

Assessing the evolution of Public Transportation demand over time based on real data through survival analysis in Bologna, Italy

Assessing the evolution of Public Transportation demand over time based on real data through survival analysis in Bologna, Italy

Changes in Fuel Prices and the Use of Public Transport: Insights from the European Union following the Invasion of Ukraine

Rising fuel prices are known to affect public transport (PT) demand. However, the impact of the oil market inflation and subsequent energy crisis following the 2022 Russian invasion of Ukraine on mobility behaviour in Europe has received little attention. This study examines shifts in fuel prices and PT mobility across 25 Europe-an Union countries between February and May 2022. Weekly variations in PT mobility were correlated with diesel and gasoline prices, and a multiple linear regression was conducted to assess the influence of contextual factors in explaining variations in PT usage. The results indicate a significant increase in PT mobility across all countries after February 2022, which was positively associated with rising fuel prices. Approximately 84% of the variance in mobility was explained, with more pronounced increases observed in countries characterised by lower telecommuting prevalence, higher housing costs relative to household income, greater price differentials between gasoline and diesel, lower adoption of alternative fuels, and higher motorisation rates. These findings suggest that the surge in fuel prices, driven by the 2022 energy crisis, may have stimulated an increase in transit ridership.

Forecasting Motor Vehicle Ownership and Energy Demand Considering Electric Vehicle Penetration

Given the increasing environmental concerns and energy consumption, the transformation of the new energy vehicle industry is a key link in the innovation of the energy structure. The shift from traditional fossil fuels to clean energy encompasses various dimensions such as technological innovation, policy support, infrastructure development, and changes in consumer preferences. Predicting the future ownership of electric vehicles (EVs) and then estimating the energy demand for transportation is a pressing issue in the field of new energy. This study starts from dimensions such as cost, technology, environment, and consumer preferences, deeply explores the influencing factors on the ownership of EVs, analyzes the mechanisms of various factors on the development of EVs, establishes a predictive model for the ownership of motor vehicles considering the penetration of electric vehicles based on system dynamics, and then simulates the future annual trends in EV and conventional vehicle (CV) ownership under different scenarios based on the intensity of government funding. Using energy consumption formulas under different power modes, this study quantifies the electrification energy demand for transportation flows as fleet structure changes. The results indicate that under current policy implementation, the domestic ownership of EVs and CVs is projected to grow to 172.437 million and 433.362 million, respectively, by 2035, with the proportion of EV ownership in vehicles and energy consumption per thousand vehicles at 28.46% and 566,781 J·km−1, respectively. By increasing the technical and environmental factors by 40% and extending the preferential policies for purchasing new energy vehicles, domestic EV ownership is expected to increase to 201.276 million by 2035. This study provides data support for the government to formulate promotional policies and can also offer data support for the development of basic charging infrastructure.

The Relationship Between Physical And Mental Workload On Job Fatigue In Maxim Car Drivers In Kupang City In 2024

Transportation is a system consisting of certain facilities along with the flow and control system that allows people or goods to move from one place to another efficiently at any time to support human activities. Indonesia as a developing country certainly has a very rapid development of services in the fields of land, sea and air transportation. Transportation owned by the Indonesian Nation certainly has innovations from past to present in order to advance transportation services, one of which is an online provider company. Online transportation companies offer transportation services such as online motorcycle taxis, online taxis, delivery of goods, food delivery and so on, thus making this service in demand by many people. Likewise in Kupang City where there are several online transportation options, one of which is Maxim car transportation which has the advantage of being quite cheap. The high demand for Maxim online transportation in Kupang City has an impact on the fatigue experienced by online transportation drivers. One of them is also felt by Maxim Mobil drivers. Based on the researcher's initial survey of Maxim drivers in Kupang city, fatigue often occurs in Maxim Car drivers due to the increase in Maxim Car users in Kupang City. This type of research is quantitative using analytic survey method and cros sectional research design. The sample in this study amounted to 81 respondents from a total population of 430 drivers selected using accidental sampling method. The results showed that there was no significant relationship between physical load and work fatigue and there was a significant relationship between mental load and fatigue in maxim car drivers in Kupang City.

High performance Calcium Copper Titanate/Polyimide dielectrics composites enabled through colloidal stabilization

AbstractTransition to electrified transportation demands advanced power electronic components with the capability to improve range, reliability, and cost of ownership to accelerate mass market adoption. Thus, improvement in the current designs, manufacturing processes, materials, and components capable of providing reliable and efficient operation at higher temperatures play important roles in meeting future needs. To address these challenges, this article focuses on novel dielectric materials designed to reduce the volume of the most important and bulky component of a power electronic system, a capacitor. A new composite dielectric material was developed by integrating the positive attributes of both polymer and ceramic capacitors to overcome the challenges of state‐of‐the‐art dielectric materials. The developed composite properties have been evaluated and showed promising results, achieving a dielectric constant of 250 at 100 Hz, 25°C, unseen in current literature. Additionally, these materials can function at high temperatures (>150°C) with good breakdown strength, providing promising working conditions for capacitors, especially in electric vehicle applications.Highlights Dielectric composites were synthesized from calcium copper titanate and polyimide. A dispersive agent showed promise in achieving a homogenous, stable mixture. Composites were prepared using tape casting. A dielectric constant of 250 was achieved at 100 Hz, 25°C. At elevated temperature, the dielectric constant increased to over 700%.

Performance of natural asphalt as a paving material: A laboratory and field evaluation

The ever-growing need to build roads to meet the necessary transportation demands is challenging, especially for developing countries. Low-volume roads (LVRs) are usually the backbone of catalyzing economic growth in these countries. With impediments surrounding Petroleum bitumen (price fluctuations) and environmental concerns, scientists are putting their effort into finding an alternative. The presented research is an attempt to check if Natural asphalt can be used as a full or partial replacement of the Petroleum bitumen. To the best of the authors' knowledge, only limited studies have focused on characterizing and understanding the engineering properties of Natural asphalt. The available techniques do not provide reliable information to the road authorities and hence they are discouraged from using it in practice. Particularly for countries, where the Natural asphalt source is available, the overall dependence on importing the Bitumen could be substantially reduced. Empirical and experience-based design criteria may not be sufficient as the standards were never developed for such materials, hence, a scientific approach is required before bringing it into practice. In this research, Natural asphalt sourced from different locations in Nigeria was assessed. Before performing the mixture level tests using Marshall and Cantabro design methods, the rheological and fatigue properties of the extracted Natural bitumen were examined in the laboratory. In the design of the experiment, various percentages of Natural asphalt were added between 0 % and 20 % by total mix weight; implying that the remaining required fraction of binder was fulfilled by the addition of petroleum bitumen. By using a ranking system (supported by statistics), an optimal design of mixture was obtained which was used in the field (exposed to normal traffic) at 30 different sections. Overall, results showed a good correlation between laboratory and field performance as compared to the petroleum-based mixture which was used as a reference mixture

Influence of Transport Process Management on Exceeding Permitted Load Weights in Transport Companies

Poland is the leader in the performance of international transport in the EU, serving more than half of its market. The most common branch of transport is road transport, predominantly heavy goods transport, which contributes to the strain on road infrastructure. Furthermore, around one-fifth of those vehicles may exceed the permissible total mass. In this respect, the increasing demand for road transport further complicates the slowdown and prevention of road network degradation. Overloads have significant consequences for road infrastructure, economics, and traffic safety. Load management in transport companies is crucial for eliminating overloads. This article aims to assess how companies conducting freight transport manage the cargo weighing process in the context of potential overloads. Data from a survey among transport industry companies in Poland were analysed. Using non-parametric U Mann-Whitney and Kruskal-Wallis tests, the influence between propensity for cargo overloading and the method of cargo weighing was examined. The results indicated that the propensity for cargo overloading in TSL industry companies is significantly associated with both the occurrence of the weighing process and its method, as well as the responsibility for this process among individual participants in the transport process.