Transport Demand Research Articles

Mobility as a Resource (MaaR) for Resilient Human-Centric Automation – A Vision Paper

With technological advances, mobility has been moving from a product (i.e., traditional modes and vehicles), to a service (i.e., Mobility as a Service, MaaS). However, as observed in other fields (e.g., cloud computing resource management) we argue that mobility will evolve from a service to a resource (i.e., “Mobility as a Resource”, MaaR). Further, due to increasing scarcity of shared-mobility spaces across traditional and emerging modes, the transition must be viewed within the critical need for ethical and equitable solutions for the traveling public (i.e., research is needed to avoid hyper-market driven outcomes for society). The evolution of mobility into a resource requires novel conceptual frameworks, technologies, processes and perspectives of analysis. A key component of the future MaaR system is the technological capacity to observe, allocate and manage (in real-time) the smallest envisionable units of mobility (i.e., atomic units of mobility capacity) while providing prioritized attention to human movement and ethical metrics related to access, consumption and impact. To facilitate research into the envisioned future system, this paper proposes initial frameworks which synthesize and advance methodologies relating to highly dynamic capacity reservation systems. Future research requires synthesis across transport network management, demand behavior, mixed-mode usage, and equitable mobility.

Determining the potential of international passenger rail services with applications to the European night train market

AbstractTransport demand represents the essential basis in the design of public transport services. Information on this is accordingly a necessary prerequisite for the preparation of supply. In the following, a general approach is presented using the example of the European night train market, with which potentials can be derived despite little knowledge of the mobility needs of the market and demand matrices can be drawn up as a basis for economic calculations. By calculating a heuristic rule, the optimal path through a network in line planning is not exclusively a function of the edge evaluations but considers the node potentials determined this way. Transport demand represents the essential basis in the design of public transport services. Information on this is accordingly a necessary prerequisite for the preparation of supply. In the following, a general approach is presented with which potentials can be derived despite little knowledge of the mobility needs of the market. In the context of graph theory, the potential of nodes is evaluated and these are included downstream to the edge selection in form of a rucksack problem. By calculating a heuristic rule, the optimal path through a network in line planning is therefore not exclusively a function of the edge evaluations but considers the node potentials as well. Based on this demand matrices can be drawn up as a basis for economic calculations. Regarding the current debate on the expansion of night train services in Europe, this approach can be adapted to the conception of an overnight train. An overview of all night trains in Northern, Central and Western Europe in 2021 is provided for this purpose and the suitability of the approach is demonstrated using an example. It will be successful in setting up a route network from which passenger flows can be derived. However, possible adaptions that can be considered as second-best solutions are not taken into account and require further development of the approach.

Research on Low-Carbon Site Selection Decision Model and Algorithm for Green Logistics

This work proposes a low-carbon site selection decision model and algorithm for green logistics. The focus is to minimize carbon emissions while optimizing the distribution network. With the development of smart shared logistics and increasing environmental awareness, there is an urgent need to tackle issues in urban logistics, including pollution, resource inefficiency, high carbon energy usage, and greenhouse gas emissions. By integrating geographic, environmental, and operational data, the proposed site selection model incorporates multi-objective optimization to balance carbon efficiency with cost-effectiveness in logistics site selection. We employ a hybrid algorithm combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to solve the complex decision-making process. The model is tested using real-world logistics data, and the results demonstrate significant reductions in carbon emissions and transportation costs when compared to conventional logistics site selection methods. Further sensitivity analysis reveals that the model is robust under varying transportation demands and geographic constraints. Also, we highlight the role of green logistics in achieving corporate sustainability goals aligning with global carbon neutrality initiatives. We believe this model offers a practical approach to fostering environmentally responsible supply chains and will contribute to the advancement of sustainable logistics operations, helping organizations make informed, eco-friendly decisions in site selection.

Public Transport Demand Elasticities in Developing Agglomerations: The Case of Tunisian Regions

Abstract This paper examined the characteristics of public transport demand in some major Tunisian cities. It followed the research trend which calculated the public transport demand elasticities. The adopted procedure in this study consists in estimating a log linear dynamic panel model relating per capita bus patronage to bus fares, users’ income, and service quality. Two variants of the basic model were estimated for the most important Tunisian agglomerations for the period 2003-2020. In the first variant, this paper considered the same fare elasticity value for the different regions, whereas in the second it’s proceeded to the calculation of specific fare elasticity values for each region. The estimation results show interesting and different elasticities compared to those revealed by earlier studies for occidental agglomerations. This result can be explained by specifics users’ characteristics and networks’ configuration. Although these results may sometimes contradict the theoretical suggestions, they still highlight the specificities of the public transport demand in Tunisia and its users’ characteristics. The demand for public transport in large Tunisian agglomerations is not very sensitive to fares changes. Second, income does not have the same effect as that advocated by economic theories. Indeed, we have found a positive impact of income on demand for public transport. Finally, the quality of service has the most important effect on demand. Changes in travel habits and cultures, improvements in the quality of services and their differentiation can be more effective for public transport than fares changes.

Comprehensive Analysis of Taxi Service Distribution, Demand Hot spots, and Air Pollution Impact: A GIS and Big Data Approach

As urban transportation demand increases, optimizing taxi services is critical for improving traffic flow and reducing environmental impact. This study investigates the spatial distribution of taxi services, demand hotspots, and their relation to air pollution using Geographic Information Systems (GIS), big data, and land use analysis. After collecting and processing taxi GPS data, we identified high-demand areas such as airports and commercial centers. We then overlaid this data with air pollution monitoring information to analyze potential correlations. The analysis revealed significant overlaps between regions of frequent taxi operation and elevated pollution levels. Using spatio-temporal models, we predicted demand patterns across different periods and explored the connection between land use (commercial, residential, and industrial zones) and traffic congestion. The results show that taxi services contribute significantly to air quality deterioration in densely populated and commercially active areas. These findings suggest that optimized taxi dispatch and fleet redistribution strategies could not only enhance operational efficiency but also alleviate air pollution in urban centers. This study provides actionable insights for policymakers and taxi companies to develop data-driven approaches to urban transportation management, reducing air pollution while improving service efficiency.

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.

Air pollution reduction co-benefits associated with low-carbon transport initiatives for carbon neutrality in China by 2060

Low-carbon transport policy measures will contribute to greenhouse gas emission reductions, as well as improvements in air quality via air pollutant emission reductions. Here, we developed a regional transport energy model by integrating a transport demand model and an energy system model to explore the associated air pollutant emission reductions achieved by low-carbon transport initiatives when projecting a path towards carbon neutrality. Several scenarios were created to identify the effectiveness and feasibility of low-carbon strategies using the Avoid–Shift–Improve (A-S-I) framework including “Technology”, “Regulation”, “Information”, and “Economy” instruments. Significant reductions in air pollutant emissions alongside transport decarbonization in China could be realized by implementing policy measures to establish effective transport demand management, a modal shift in transport use, and technological improvements. In particular, the “Improve” strategy was more effective in delivering significant reductions in air pollutant emissions than were the “Avoid” and “Shift” strategies, but the policy effects of each A-S-I pillar varied greatly for different pollutants and across different regions. An air pollution reduction strategy for the transport sector should be designed in an integrated manner based on a series of different strategies and tools.

Optimization of Mechanical Deployable Reentry Vehicle based on Multi-Fidelity Aerodynamic-Trajectory Coupling Model

As the aerospace industry continues to advance, the demand for round-trip transportation between Earth and space, as well as deep space exploration missions, is increasing. The mechanical deployable reentry vehicle, a complex multidisciplinary system, experiences parameter coupling between trajectory design and aerodynamic simulation. The calculation of trajectory and the design of aerodynamic layout directly impact the flight process. To address issues such as biased trajectory data obtained without considering disciplinary coupling and the high computational costs associated with overall aerodynamic layout optimization, this paper fully considers the impact of global aerodynamic drag on the flight trajectory. A multi-fidelity optimization model for reentry vehicles, named Multi-Fidelity Aerodynamics-Trajectory Coupling (MF-ATC), is proposed. The model uses a large number of low-accuracy model samples to control computational costs, without considering the coupling relationship, and a small number of high-accuracy model samples to ensure approximation accuracy, achieving fast and precise prediction of flight trajectories. The results demonstrate that the MF-ATC model improves trajectory prediction accuracy and efficiently accomplishes aerodynamic shape optimization, effectively enhancing the vehicle's performance. The model shows great potential for engineering applications.

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.

A linear model to estimate modal split in international freight transport, based on revealed preferences about cost and time saving

We present a new model for estimating the distribution of international freight transport over transport modes that is directly applicable to aggregated data, allows estimations and predictions also when some modes are infeasible on some routes, and requires few processing time and memory space. It builds on the assumption that demand for transport by a given mode is driven by trading firms’ and consumers’ preferences about saving transport costs and time. In contrast to conventional mode-choice models, it is linear and grounded on consumer demand theory. Applying the model to international freight transport as recorded in the latest upgrade of UN Comtrade reveals an average cost elasticity of transport demand of − 0.32 and an average time elasticity of − 0.18. In addition, we find significant independent mode-specific effects. Cost and time elasticities are highly dependent on the type of commodity transported. The cost elasticity ranges from zero to − 1.9 and the time elasticity from zero to − 3.3 across commodity groups defined at the four-digit level of the Harmonized System classification. These findings suggest that policy measures, exogenous shocks or other events that change the relative transport costs and transit times across modes can cause modal shifts—for some commodities more than for others—thereby mitigating the loss in welfare.

Driving range estimation for electric bus based on atomic orbital search and back propagation neural network

AbstractAs urbanization and transportation demands continue to increase, electric buses play an important role in sustainable urban development thanks to their advantages of emission reduction, noise and pollution reduction. However, electric buses still face some challenges, in which, range anxiety is one of the main factors limiting its popularization. To solve this problem, an accurate estimation method for the driving range of electric buses based on atomic orbital search (AOS) algorithm and back propagation neural network (BPNN) was used, in which a long‐term bus operation dataset under different driving conditions is utilized to train BPNN, and then weight and bias are taken as the first generation provided for AOS approach to find a more appropriate parameter combination. Simulation and experimental analysis show that the algorithm introduced in this paper has higher prediction accuracy and efficiency compared to the traditional machine learning algorithms, that compared with BPNN, AOSBP reduced MAE, RMSE and MAPE by 85.6%, 50.9% and 64.6%, respectively, which effectively relieves range anxiety, and ensures the normal operation of the electric bus fleet.

Research on the construction of novel nano‐modified clean fracturing fluid and the migration characteristics of proppants

AbstractClean fracturing fluid exhibits desirable characteristics, such as low residue, easy flowback, minimal reservoir damage, and favorable rheology, making it highly adaptable. However, challenges remain in terms of high cost, large consumption, and poor stability. Hence, this study focuses on incorporating SiO2 nanoparticles into clean fracturing fluid to develop a novel nano‐modified system. Experimental tests were conducted to analyze the impact of mass fractions of hexadecyltrimethylammonium chloride (CTAC), sodium salicylate (Nasal), and SiO2 nanoparticles on the viscosity of the solution. The results indicate a significant increase in viscosity with higher mass fractions of SiO2. A nano‐modified clean fracturing fluid composition of 1.5 wt% CTAC, 0.4 wt% Nasal, and 0.1 wt% SiO2 was selected as it meets the technical specifications for fracturing fluid while exhibiting excellent elastic modulus (2.279 Pa) and viscous modulus (0.818 Pa), thereby displaying superior viscoelastic properties. Furthermore, the system maintains a viscosity above 20 mPa.s under temperature (70°C) and shear stress (170 s−1), indicating robust temperature and shear resistance to meet the demands of downhole fracturing and proppant transport. Based on rheological experimental data, a Carreau rheological model was established for the nano‐modified clean fracturing fluid. Considering particle interaction, wall blockage, and fluid filtration, a settlement model was modified with a correction coefficient, leading to the construction of a mathematical model for correcting proppant particle settling in fractures. The error between measured settlement velocity and calculated values remained within 10%. Finally, the migration law of proppant is studied, and the settlement law of proppant in fracturing fluid and fracture is clarified.

Influence of V and V–N Addition on the High‐Cycle Fatigue Performance for EH40‐Grade High‐Strength Hull Structure Steels

As the demand for polar ships with long‐term service in ice environment increases, the steel materials are required to have key properties of high strength, toughness, and fatigue properties. The mechanical properties of hull structure steel with three different composition systems are investigated by tensile test, impact test, hardness test, and high‐cycle fatigue test, and the microstructure is observed and analyzed by optical microscope, scanning electron microscope–electron backscatter diffraction, and transmission electron microscope. The results show that the addition of V and V–N elements improves the mechanical properties of steel plates, especially the fatigue performance. The fatigue limits of V0, V and V–N steels are 250, 275, and 292 MPa, respectively. The microstructures of the steel plates are composed of ferrite and bainite, and the proportion of ferrite in steels increases with the addition of V and V–N elements. The fatigue cracks initiate in the soft phase nearby the soft/hard phase interface and propagate along the soft phase to form transgranular fracture. The fatigue limit of steels is elevated by doping V and V–N elements which increases the strength of ferrite. Moreover, the enhancement effect of V–N compound addition is better than that of V individual addition.

A Study on Maximizing Traffic Reachability in Contingency Scenarios Based on Genetic Algorithms

This paper presents a study on maximizing traffic demand accessibility in transportation networks under contingency scenarios using genetic algorithms. Transportation demand accessibility is defined as the proportion of allocated traffic that can reach its destination, considering potential disruptions in the network. The study begins by formulating a model for calculating transportation demand accessibility between different origin-destination pairs, taking into account the possibility of unforeseen events on any road segment. By applying genetic algorithms, the study aims to optimize the allocation of traffic flows in a way that maximizes the expected reachability across the network, even when multiple road sections experience contingencies. Several scenarios are considered, including the introduction of capacity constraints on road segments and varying traffic demands. The results demonstrate that the proposed method can effectively optimize traffic flow allocation, enhancing the robustness and efficiency of transportation networks under uncertain conditions. This research provides valuable insights for urban planners and traffic managers seeking to improve network resilience in the face of unexpected disruptions.

Exploring the dynamics of the transport system energy consumption in Australia − A preliminary systems modelling effort

In the face of an ever-increasing trend in the Australian transport demand with minimal improvement in fuel efficiency, petroleum products continue to dominate the primary energy source for the transport sector. The persistence of this reliance hinges on the urgent formulation and robust implementation of effective policies. This paper endeavours to elucidate how the application of system dynamics modelling can facilitate the development and assessment of efficacious policies to steer the trajectory of transport energy consumption in Australia. To illustrate this approach, we present two compelling case studies − the urban passenger transport system in New South Wales (NSW) and the freight transport system in Australia. The first case study explores the intricate dynamics of urban passenger transport mode choice, which shapes different pathways for energy consumption. These pathways are influenced by relative vehicle utility, corresponding to changes in GDP per capita, congestion levels, toll and fuel costs, and historical travel patterns. The second case study delves into Australia’s complex dynamics of heavy vehicle fleet transition. This transition is contingent on many variables, such as changes in GDP, freight tasks, infrastructure capacity requirements, operational and capital costs of various heavy vehicle classes, and fluctuating fuel costs, among others. Simulation results present the dynamic interplay between supply and demand factors. This showcases how an integrated system dynamics model of the transport system can be developed as a holistic policy assessment tool to achieve a balanced energy consumption in the transport system and thereby the sustainability goals. This study calls for more in-depth research about this problem to support policymakers with a decision-support tool to assist scenario analysis and intervention design over time toward achieving sustainable development goals within Australia’s transport system.

Mission-Oriented Research and Theory of Change: Driving Australia’s Transition to a Circular Economy

AbstractAustralia’s linear economic system hinders the collective pursuit of sustainability, equity, and well-being. As demands for housing, food, energy, transportation, and goods escalate, transitioning to a circular economy becomes imperative. Such a transition necessitates a transformation in business and governance models, as well as cultural shifts, to foster sustainable material flows. The proposed change will likely unfold in five phases: envisioning a circular economy future, evaluating options and trade-offs, initiating local actions, amplifying national efforts, and solidifying global agreements. This shift will occur within the context of significant technical, environmental, social, and economic megatrends, and each phase is likely to overlap with the next, with some phases occurring concomitantly and ongoing. It will reshape socio-technical systems and social practices that fulfil our essential needs. Moreover, this transformation process is inherently circular, characterised by continuous cycles of learning, adaptation, and risk management.

Systematic Analysis of Commuting Behavior in Italy Using K-Means Clustering and Spatial Analysis: Towards Inclusive and Sustainable Urban Transport Solutions

Transport Demand Management (TDM) is crucial in shaping travel behavior and enhancing urban mobility by promoting sustainable transport options. This study represents a comprehensive analysis of employee commuting behavior across seventy-seven cities in Italy, with a focus on Rome as a case study. It investigates some requirements of the workplace travel plan as a TDM strategy for promoting sustainable commuting. An online survey conducted in June 2022 yielded 2314 valid responses, including 1320 from private car drivers. K-means clustering was used to identify distinct behavioral patterns among commuters, revealing four clusters based on demographic factors and transport preferences, such as age, gender, family circumstances, vehicle ownership, willingness to walk, ride bicycles, or e-scooters, and reasons for mode choice. This study analyzed Rome’s public transport network, land use, and private car use. Results underscore the need for tailored transport policies that enhance inclusivity and accessibility, especially for employees with family members who cannot commute independently. A spatial analysis of Rome reveals significant infrastructure deficiencies, such as complicated transfers and inaccessible stations, which discourage PT use. Future research should explore the impact of remote work and psychological factors and conduct in-depth subgroup analyses to inform inclusive transport policy development.

State-of-Health Estimation for Lithium-Ion Batteries in Hybrid Electric Vehicles—A Review

This paper presents a comprehensive review of state-of-health (SoH) estimation methods for lithium-ion batteries, with a particular focus on the specific challenges encountered in hybrid electric vehicle (HEV) applications. As the demand for electric transportation grows, accurately assessing battery health has become crucial to ensuring vehicle range, safety, and battery lifespan, underscoring the relevance of high-precision SoH estimation methods in HEV applications. The paper begins with outlining current SoH estimation methods, including capacity-based, impedance-based, voltage and temperature-based, and model-based approaches, analyzing their advantages, limitations, and applicability. The paper then examines the impact of unique operating conditions in HEVs, such as frequent charge–discharge cycles and fluctuating power demands, which necessitate tailored SoH estimation techniques. Moreover, this review summarizes the latest research advances, identifies gaps in existing methods, and proposes scientifically innovative improvements, such as refining estimation models, developing techniques specific to HEV operational profiles, and integrating multiple parameters (e.g., voltage, temperature, and impedance) to enhance estimation accuracy. These approaches offer new pathways to achieve higher predictive accuracy, better meeting practical application needs. The paper also underscores the importance of validating these estimation methods in real-world scenarios to ensure their practical feasibility. Through systematic evaluation and innovative recommendations, this review contributes to a deeper understanding of SoH estimation for lithium-ion batteries, especially in HEV contexts, and provides a theoretical basis to advance battery management system optimization technologies.

Pre-occurrence location-allocation-configuration of maritime emergency resources considering shipborne unmanned aerial vehicle (UAV)

Demand for maritime transportation has constantly increased throughout recent years due to its high capacity, extensive coverage, high safety, and so on. However, the growth has also contributed to a rise in maritime accidents, highlighting the need for developing maritime emergency management strategies. In maritime emergency management, resource location-allocation-configuration is the most important and inseparable preparedness action before disasters, directly deciding the quality of emergency rescue operations. Traditional ship-only rescue is frequently both cost-intensive and time-consuming, seriously affecting rescue performance. This study introduces an innovative shipborne UAV operation system to address these limitations. Firstly, a multi-objective mixed-integer nonlinear programming model is established to minimize total rescue time and cost by considering the uncertain maritime environment, continuous docking placements of ships, UAV flight distance, rescue station capacity, etc. Secondly, to efficiently obtain an execution plan, a two-stage algorithm is proposed to facilitate our mission optimization model. Large-scale simulated instances and a real case study demonstrate that (i) the proposed algorithm can solve the model efficiently. (ii) the application of shipborne UAV operation system in maritime emergencies shows a significant improvement in time and cost compared to ship-only system. (iii) it is necessary to consider the uncertain maritime environment when solving emergency management problems. Moreover, extensive parameter sensitivity analysis provides managerial insights into the impact of various factors on the optimization outcomes. We also explore deeper insights that may benefit maritime administration's decision support.

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

The 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.