Transport Strategies Research Articles

A Literature Review of Distribution Solving Problems by Various Transportation Methods

The aim of this study to review the limited survey of a systematic various existing transportation problems and the application developed by different researchers and applied in some case studies. The methodology applied in this paper through various research papers that available at Google Scholar, Science direct, Elsevier, IOP and another publisher from 2014-2022 (twenty-four international researches). The main purpose of this review paper is to discuss, analyze and the existing forms of various types of transportation problems and their systematic development, Findings show that in addition to the MODI and VAM methods, other methods have also emerged to solve the transportation problem. One of them is Improve VAM (IVAM) that built by Nahar et.al (2018). IVDM on transportation or delivery strategy. Further, this paper concludes that mathematical models and algorithm method of transportation derivatives/techniques of MODI and VAM are most used to optimize single or multipurpose parameters such as distribution cost, profit earned, total transportation time etc.

A flexible cost model for seaport-hinterland decisions in container shipping

A seaport–hinterland system offers a wide range of service options for shippers and consignees to export or import containerized cargo. The present study, set within the context of Brazil, seeks to propose a flexible cost model to aid joint seaport–hinterland network decisions by freight forwarders and clients. More specifically, employing simulation and sensibility analysis, the proposed model is employed to explore: (i) decisions on whether to perform logistics services in seaports or dry ports; (ii) decisions on whether to transport containerized cargo by road or by multimodal transportation (road and rail); and (iii) decisions on whether cargo is best delivered or received in either consolidated or deconsolidated form. Simulations are undertaken against seven transportation strategy permutations via which service operators might deliver containers from seaports or dry ports to clients. We find five factors—(i) cargo value, (ii) cargo accommodation type, (iii) transportation mode, (iv) delivery distance and (v) shipping process time—as critical to joint seaport–hinterland operations impacting shippers and consignees. Furthermore, dry ports are shown to be particularly cost efficient for imports requiring long-term storage, even where the dry ports are not directly connected to seaports or intermodal facilities.

A Theoretical Framework for Developing a National Transportation Strategy

Developing the surface transportation systems the United States needs will require asking and answering a series of questions, beginning with the most basic:What is the main purpose of surface transportation?What resources are available and what is the best way to deploy those resources?How do we measure success?Determining what future transportation systems should look like will require analyzing a range of economic, demographic, technological and socio-political variables.As management guru Peter Druker famously said, “The primary goal of every enterprise should be to create customers”. Transportation leaders must undertake the critical task of determining what transportation customers want and are willing to pay for. In doing so, they must understand that there are many categories of customers—such as commuters, businesses, and logistics companies—each with different priorities. Measurements of success should also be customer-focused.Finally, state departments of transportation—not their federal counterparts—are often best positioned to ask these questions and develop the right answers for their own states.

Modelling driver's response to demand management strategies for electric vehicle charging in Australia

Electric vehicles (EVs) can help decarbonise transport as long as the energy sector successfully balances the electricity supply with the demand and maximises the use of renewable resources. For that, demand-side management strategies are necessary to encourage users to charge at certain times through time-of-use (ToU) tariffs or to control the load provided for charging (i.e., supplier-managed charging). However, adapting to different charging times can disrupt users' schedule flexibility. This study investigates consumer preferences for smart charging technology and control (user-managed or supplier-managed) and responses to progressive ToU tariff discounts for guiding changes in EV charging time in Australia. We analyse the potential of ToU tariffs in shifting demand to late-night hours and around midday when there is a peak in solar energy generation. Based on a sample of 994 drivers (including 97 EV owners), we estimated a multinomial choice model to identify key predictors of individual preference for smart charging management and a bivariate ordered model to investigate consumer response to time-of-use discounts. The results show that activity-travel behaviour is an important predictor of both demand-side management strategies. Consumers willing to change EV charging time in response to ToU tariffs are likely to have more flexible schedules, while those with more time constraints seek the practical benefits of smart charging. Current EV owners have higher propensities than potential adopters to choose supplier-managed smart charging and charge around midday in response to ToU tariffs. This indicates that trials with current EV owners may overestimate acceptance of these strategies. Our findings show that while monetary incentives can successfully shift an important share of consumers to night-time hours, these incentives are not very effective in shifting to midday charging. Synergy in formulating transport and energy demand strategies may be necessary to facilitate such a shift.

Hierarchical Reinforcement Learning for Crude Oil Supply Chain Scheduling

Crude oil resource scheduling is one of the critical issues upstream in the crude oil industry chain. It aims to reduce transportation and inventory costs and avoid alerts of inventory limit violations by formulating reasonable crude oil transportation and inventory strategies. Two main difficulties coexist in this problem: the large problem scale and uncertain supply and demand. Traditional operations research (OR) methods, which rely on forecasting supply and demand, face significant challenges when applied to the complicated and uncertain short-term operational process of the crude oil supply chain. To address these challenges, this paper presents a novel hierarchical optimization framework and proposes a well-designed hierarchical reinforcement learning (HRL) algorithm. Specifically, reinforcement learning (RL), as an upper-level agent, is used to select the operational operators combined by various sub-goals and solving orders, while the lower-level agent finds a viable solution and provides penalty feedback to the upper-level agent based on the chosen operator. Additionally, we deploy a simulator based on real-world data and execute comprehensive experiments. Regarding the alert number, maximum alert penalty, and overall transportation cost, our HRL method outperforms existing OR and two RL algorithms in the majority of time steps.

Path planning for dual UAVs cooperative suspension transport based on artificial potential field-A* algorithm

Unmanned Aerial Vehicles (UAVs) are frequently utilized for transferring and delivering. Dual- or multi-quadrotor cooperative transportation schemes are required for heavy loads. In this work, focusing on the path planning for a dual-quadrotor cooperative transport system, a unique artificial potential field-A*(APF-A*) algorithm is studied to fulfill the safe and effective cooperative suspension transport. APF-A* algorithm proposed the strategy of host-wingman cooperative transportation based on the APF theory, and optimizes the heuristic function by introducing the concept of repulsive force to maintain the quadrotor flying at a safe distance from the obstacle. In addition, APF-A* algorithm promises to increase search efficiency while identifying the optimal path, and it can reduce the swing angle motion of loads to enhance the security of transportation. The coordinated flight control and path planning of quadrotor UAVs are simulated in AirSim environment and tested in flight experiments. The results demonstrate that the algorithm is capable to plan safe and reliable paths, meanwhile changing formation and avoid collision when the wingman is close to obstacles. In comparison to the regular A* algorithm, the APF-A* algorithm proposed in the paper performs better in path planning for dual-quadrotor cooperative transport system, reducing the search time and the number of search grids by nearly 60% and 53.75%, respectively.

Review of South Africa’s public transport system

This paper provides a review of public transport policy and strategy initiatives in South Africa for the period 1996 to 2021 and reviews the progress made in public transport provision against the policy guidelines and strategies of the 1996 White Paper on National Transport Policy, relevant strategies, and legislation.Formal public transport is increasingly losing market share against the informal minibus taxi industry. In the 2021 National Household Travel Survey minibus taxis accounted for 80.2% of work trips in 2020 compared to 67.2% in 2013 and 63% in 2003 while buses accounted for 16.6% (19.5% in 2013 and 22% in 2003) and train 3.2% (12.9% in 2013 and 15% in 2003) of work trips respectively.The research finds that a lack of policy implementation is at the center of the issues experienced in public transport in South Africa together with a lack of adequate transportation planning and financing. Most public transport users are not served by BRTs and the high-speed Gautrain services and have become largely dependent on the informal, lightly regulated, minibus taxi industry for their transportation needs. Users are dissatisfied with public transport services in the country. Policy objectives set out in government policy documents are, in general, not being achieved.

A discrete-time distributed optimization algorithm for cooperative transportation of multi-robot system

Multi-robot cooperation is a typical application of multi-robot system, which has strong potential applications in many special occasions. However, few scholars have considered cooperative optimization transportation from the perspective of optimization. This paper investigates the cooperative transportation optimization problem for multiple mobile robots with kinematically redundant manipulators. First, a discrete-time distributed optimization algorithm with fixed step size is proposed to achieve linear convergence. Second, by introducing a rotation variable into the optimization problem, the joint angular velocity of manipulator and the velocity of moving platform change more smoothly. Third, a virtual leader-follower transport strategy is used in this paper to improve the stability of the multi-robot system. In the physical experiment, the trajectory of each end-effector matches the expected trajectory, and the center trajectory of the transport object almost coincides with the virtual leader trajectory with acceptable error. Moreover, the simulation and physical example are provided to demonstrate the effectiveness of the proposed algorithm.

Topical Issues of Formation and Development of Moscow Interregional Transport Prosecutor’s Office

The objective of the study of issues related to participation of transport prosecutors in solving the problems of ensuring the rule of law in transport is to comprehend the role and significance of the transport prosecutor’s office in the history of Russia and at the present stage of its development, to identify the most significant areas of activity of transport prosecutors and effective means of its implementation in the interests of implementing national tasks of socio-economic development, implementation of the provisions of the Transport Strategy of the Russian Federation until 2030 with a forecast until 2035.Based on the application of the historical and legal method, the stages of formation and development of the transport prosecutor’s office in Russia are identified, the regular relationships between development of the transport industry and the change in the role and legal status of transport prosecutors are identified, conclusions are drawn regarding the significance of development of the transport prosecutor’s office in the modern period and the possibility of using the historical experience of legal regulation of relations with participation of transport prosecutors to solve urgent problems of transport system development.The formal dogmatic and statistical analysis made it possible to reveal the main areas of activity of transport prosecutors, to formulate, based on an analysis of the work of Moscow Interregional Transport Prosecutor’s Office, conclusions about the most promising forms, methods and means of exercising the powers of transport prosecutors to ensure the rule of law in transport sector.The use of systemic legal analysis made it possible to analyse the state and prospects for further interaction between Moscow Interregional Transport Prosecutor’s Office and departments of Russian University of Transport for formation of a legal culture of future transport specialists, legal education of students and young people, to determine ways and means of solving the problems of civil law education of students using the potential and considering possible correlation of the efforts of the professors of legal disciplines of RUT University and transport prosecutors.The implementation of individual proposals formulated by the authors can contribute to the increment of scientific knowledge about the role, directions, and forms of work of transport prosecutors, improve further interaction between Moscow Interregional Transport Prosecutor’s Office and departments of Russian University of Transport to ensure the rule of law in transport, the implementation of the Cooperation Agreement between Moscow Interregional Transport Prosecutor’s Office and Russian University of Transport.

Non-Invasive Micro-Test Technology and Reciprocal Grafting Provide Direct Evidence of Contrasting Na+ Transport Strategies between Cucurbita moschata and Cucurbita maxima

Cucurbita species are widely used as rootstocks for cucumber, watermelon, and other plants for the restriction of toxic Na+ transport from root to shoot. Previous studies have found distinct salt tolerance strategies between Cucurbita moschata and Cucurbita maxima; the former accumulates a large amount of Na+ in the root, while the latter accumulates Na+ in the shoot. To further study the mechanism of Na+ transport in plants, four reciprocal grafts were made between MB (C. moschata) and JHL (C. maxima), denoted as MB/MB, JHL/JHL, MB/JHL, and JHL/MB (scion/rootstock). The results showed that using MB as the rootstock effectively reduced the accumulation of Na+ in the scion. Conversely, JHL rootstock tended to transfer more Na+ to the scion. To clarify this phenomenon, the velocities of Na+ flows on the root surface, stem, and vein of grafting combinations were measured using non-invasive micro-test technology. Compared with the seedlings using JHL as rootstock, the grafted combination with MB as rootstock had a higher root Na+ efflux and lower Na+ fluxes in the stem and vein. qRT-PCR analyses revealed the critical roles of salt overly sensitive 1 and high-affinity potassium as components of the mechanism enabling Na+ exclusion from the root and Na+ unloading from the stem xylem. Compared with the seedlings using MB as rootstocks, the JHL-grafted plants showed more rapid stomatal closure and decreased transpiration rate in the first three hours after salt stress but maintained a higher level under prolonged salt treatment (120 h). The tissue tolerances of JHL and MB were assessed using the isolated leaves under NaCl to exclude the influence of the root and stem. The results showed that the salinity inflicted more serious damage to MB leaves than to JHL leaves. qRT-PCR analyses indicated that the intracellular Na+/H+ transporter in the leaf vein was involved in this process. All these findings indicated that C. moschata and C. maxima adopted different strategies for regulating Na+ transport, and grafting can be used as a tool to create more salt-tolerant plants.

How do the affective and symbolic factors of private car driving influence car users’ travel behavior in a car restriction policy scenario?

The number of private car trips a person makes is usually linked to external factors such as economic incentives or disincentives, legislation, infrastructure, and the quality of transport systems, among others. The impact of these options are typically analyzed to evaluate urban transport policies and projects. However, internal factors related to an individuals' attitudes and perceptions also play an essential role in the amount of private car driving they do and on urban mobility as well. Therefore, insight into private car driving's affective and symbolic aspects is important in order to formulate appropriate transport policies and strategies to reduce car-use dependency and encourage private car drivers to use public transport. Congestion charging schemes make drivers pay the full social cost of driving, they achieve congestion reduction, pollution reduction, they make cities more attractive for pedestrians and cyclists, and help improve quality of life. Congestion charging schemes are often difficult to implement and expensive to administer. As a result of these reasons, several cities worldwide have adopted a variety of car restriction schemes that try to reduce air pollution and congestion as well, instead of establishing congestion charges. The effects of driving restrictions in the short term are positive, but in the medium and long term, there are unwanted consequences. However, by giving private car drivers the option to pay a toll so that their cars are exempted from the restriction, these perverse incentives could be eliminated. This paper analyzes the impacts of internal factors related to individuals' attitudes and perceptions about the travel behavior of car users affected by car restriction policies in urban areas. We designed a stated preference survey conducted among car owners in Cali, Colombia, where a License Plate Restriction Charging (LPRC) policy has been in place since January 2017. Through hybrid discrete choice modeling, we demonstrated that latent variables, such as the feelings of being in control, independence, and higher social status, positively influence the decision to use cars for daily trips, thus impacting the urban modal split. The heterogeneity captured through these latent variables allowed us to understand more deeply how individuals deal with the LPRC policy in order to travel to their destination.

The information value of logistics platforms in a freight matching market

The information asymmetry in the freight matching of the long-haul trucking industry usually pushes shippers to join a logistics platform that has better knowledge of truckers’ cost information. A logistics platform that is plugged into a freight transportation system helps shippers overcome their information disadvantage but produces the double marginalization effect. We investigate the equilibrium characteristics of a shipper and logistics platform in the freight matching market and find that the shipper prefers to cooperate with the logistics platform in a small-scale sales market but searches for truckers by himself in a large-scale sales market. We show that the information value of a logistics platform for the freight system consists of two aspects: (i) employ low-cost truckers for the shipper and avoid the shipper’s inefficient expansion of the selling quantity in a moderate-scale sales market with greatly heterogeneous truckers, and (ii) improve both the total profit and social welfare of the freight system in a small-scale sales market. We further find that the shipper’s private information may hurt the logistics platform and the logistics platform can mitigate her double marginalization disadvantage by improving value-added services or lowering the logistics price in a large-scale market. Our findings provide guidelines for the shipper and logistics platform on how to choose the appropriate transportation strategy and employment strategy in different logistics markets.

Research progress on iron absorption, transport, and molecular regulation strategy in plants.

Iron is a trace element essential for normal plant life activities and is involved in various metabolic pathways such as chlorophyll synthesis, photosynthesis, and respiration. Although iron is highly abundant in the earth's crust, the amount that can be absorbed and utilized by plants is very low. Therefore, plants have developed a series of systems for absorption, transport, and utilization in the course of long-term evolution. This review focuses on the findings of current studies of the Fe2+ absorption mechanism I, Fe3+ chelate absorption mechanism II and plant-microbial interaction iron absorption mechanism, particularly effective measures for artificially regulating plant iron absorption and transportation to promote plant growth and development. According to the available literature, the beneficial effects of using microbial fertilizers as iron fertilizers are promising but further evidence of the interaction mechanism between microorganisms and plants is required.

Fuzzy PID control system optimization and verification for oxygen-supplying management in live fish waterless transportation

Live fish waterless transportation could be recognized as an essential supplement for water-based transportation due to its low oxygen consumption and less waste water pollution. The critical problem to maintaining the fish survival quality under such a unique transport strategy is accurately controlling the oxygen concentration in the container to be constantly at stable and high levels. This paper aims to propose an improved fuzzy PID control system based on the grey model with residual rectification by improved particle swarm optimized Gated Recurrent Unit (GM-IPSO-GRU) to realize advanced oxygen level control. In addition, it is also reinforced by adopting the improved grey wolf optimization (IGWO) for the majorization of control parameters (quantization factors, scale factors) with full consideration of fish size features. In this study, Turbot (Scophthalmus maximus) is taken as the test subject to verify the integrated control performance of the optimized fuzzy PID controller through simulated waterless live transportation under low-temperature conditions. The proposed control system is validated as more efficient than the traditional proportional integral derivative (PID) and fuzzy PID algorithms for handling its nonlinear, time-varying, and time lag problems well. In summary, the control group experiment shows that the newly-designed control system has the advantages of shorter stabilization time, minor overshoot, and strong anti-interference ability for oxygen level adjustment. Finally, applying this novel control technology can effectively improve oxygen adjustment efficiency and provide feasible quality control support for the deep optimization of the live fish circulation industry.

Accelerated nonlinear domain decomposition solver for multi-phase flow and transport in porous media

Development of robust and efficient nonlinear solution strategies for multi-phase flow and transport within natural porous media is challenging. Fully Implicit Method (FIM) is widely used in reservoir simulation for solving nonlinear algebraic systems. Previous studies showed that nonlinearities of flow and transport problems could exhibit large degrees of locality across timesteps and nonlinear iterations. Nonlinear domain decomposition (NDD) is an attractive class of methods for achieving localization and resolving spatially unbalanced nonlinearities. However, NDD is not robust as iterative solvers, because the outer iterations are essentially fixed-point type.We design a solution framework to accelerate the outer-loop convergence of NDD. We first apply a nonlinear acceleration (NA) technique to the fixed-point iterations, for correcting the boundary conditions of subdomains. Because flow problems are parabolic and exhibit global behaviors, long-range information is essential to capture main features of the flow field. We incorporate a preconditioning step based on CPR-AMG before each timestep, to provide better initial solutions. Additionally, smoother iterations can be taken to improve the boundary conditions during the NDD process.We evaluate the NDD methods using heterogeneous multi-phase models. The nonlinearities associated with the flow, transport and the coupling are locally resolved through the NDD iterations. Results demonstrate that the basic NDD solver is afflicted with severe nonlinear difficulties. By comparison, the accelerated NDD solver greatly improves the outer convergence performance. Moreover, the new solver can effectively exploit locality on the Newton level, for both the flow and transport problems.

ПРОБЛЕМЫ И ОСОБЕННОСТИ РЕАЛИЗАЦИИ ТРАНСПОРТНОЙ СТРАТЕГИИ РОССИЙСКОЙ ФЕДЕРАЦИИ ДО 2035 ГОДА

Strategic planning for the development of the transport system is an important com- ponent of managing the integrity and connectivity of the economic space of the coun- try and its regions. Therefore, the purpose of the article was to analyze the problems and features of the implementation of the Transport Strategy of the Russian Federa- tion until 2030 with a forecast until 2035. During the analysis of the achievement of the targets of the Transport Strategy implemented until 2021, it was found that most of them have not been achieved. The main reasons for this were changes in external political and economic conditions, as well as insufficient funding. In the new edition of the strategy, the directions of development of transport systems were changed: emphasis was placed on trade with Asian countries, as well as on the development of interregional and external connectivity of the economic space. In the new edition of the strategy, the directions of development of transport systems were changed: em- phasis was placed on trade with Asian countries, as well as on the development of in- terregional and external connectivity of the country's economic space. The main risks of non-implementation of the strategy were highlighted: rising prices for construction materials; the introduction of financial and transport sanctions that restrict the devel- opment of transport in Russia; inconsistency of regulatory documents that are tools for implementing the goals of the Transport Strategy; the difficulty of attracting ex- tra-budgetary financing. The results of the work can be useful to researchers and ex- ecutive authorities that manage the transport system

Robust vehicle routing with drones under uncertain demands and truck travel times in humanitarian logistics

Resource transport in the aftermath of disasters is critical, yet in the absence of sufficient historical data or accurate forecasting approaches, the development of resource transport strategies often faces the challenge of dealing with uncertainty, especially uncertainties in demand and travel time. In this paper we investigate the vehicle routing problem with drones under uncertain demands and truck travel times. Specifically, there is a set of trucks and drones (each truck is associated with a drone) collaborating to transport relief resources to the affected areas, where a drone can be launched from its associated truck at a node, independently transporting relief resources to one or more of the affected areas, and returning to the truck at another node along the truck route. For this problem, we present a tailored robust optimization model based on the well-known budgeted uncertainty set, and develop an enhanced branch-and-price-and-cut algorithm incorporating a bounded bidirectional labelling algorithm to solve the pricing problem, which can be modelled as a robust resource-constrained vehicle and drone synthetic shortest path problem. To enhance the performance of the algorithm, we employ subset-row inequalities to tighten the lower bound and incorporate some enhancement strategies to quickly solve the pricing problem. We perform extensive numerical studies to assess the performance of the developed algorithm, discuss the benefits of considering uncertainty and robustness, and analyse the impacts of key model parameters on the optimal solution. We also evaluate the benefits of the truck–drone collaborative transport mode over the truck-only transport mode through a real case study of the 2008 earthquake in Wenchuan, China.

Cross-Modality Earth Mover’s Distance for Visible Thermal Person Re-identification

Visible thermal person re-identification (VT-ReID) suffers from inter-modality discrepancy and intra-identity variations. Distribution alignment is a popular solution for VT-ReID, however, it is usually restricted to the influence of the intra-identity variations. In this paper, we propose the Cross-Modality Earth Mover's Distance (CM-EMD) that can alleviate the impact of the intra-identity variations during modality alignment. CM-EMD selects an optimal transport strategy and assigns high weights to pairs that have a smaller intra-identity variation. In this manner, the model will focus on reducing the inter-modality discrepancy while paying less attention to intra-identity variations, leading to a more effective modality alignment. Moreover, we introduce two techniques to improve the advantage of CM-EMD. First, Cross-Modality Discrimination Learning (CM-DL) is designed to overcome the discrimination degradation problem caused by modality alignment. By reducing the ratio between intra-identity and inter-identity variances, CM-DL leads the model to learn more discriminative representations. Second, we construct the Multi-Granularity Structure (MGS), enabling us to align modalities from both coarse- and fine-grained levels with the proposed CM-EMD. Extensive experiments show the benefits of the proposed CM-EMD and its auxiliary techniques (CM-DL and MGS). Our method achieves state-of-the-art performance on two VT-ReID benchmarks.

Interval-valued Fermatean fuzzy heronian mean operator-based decision-making method for urban climate change policy for transportation activities

Climate change affects the world. Due to excessive GHG emissions, urban transportation contributes to this threat. Policymakers and authorities want to reduce transportation-related GHG emissions. An imaginary urban area with high transportation-related greenhouse gas emissions, dense, interconnected transportation modes, and a high population density is considered. Istanbul, Turkey meets the criteria of this imaginary place, so the case analysis considers this city. Istanbul’s decision-makers are looking for effective strategies to prioritize urban climate change policy alternatives for transportation activities. Four alternative strategies and 13 criteria are presented in this context. Innovative multi-criteria decision-making (MCDM) method with the interval-valued Fermatean fuzzy sets (IVFFSs) strategies is proposed for advantage-prioritization so decision-makers can select the most effective strategies for policies. Utilizing the IVFFSs, the proposed method effectively tackles the qualitative/quantitative data and uncertain information that occurs in realistic applications. In this study, firstly IVFF-heronian mean operators with their desirable characteristics are presented to aggregate the IVFF information. The proposed operators can overcome the drawbacks of existing IVFF information-based operators by considering the relationships between IVFF numbers. Based on IVFF heronian mean operators, a hybrid decision-making framework is proposed by integrating criteria importance through inter-criteria correlation (CRITIC), rank sum (RS), and the double normalization-based multi-aggregation (DNMA) methods with IVFF information. In this method, the CRITIC and RS methods are implemented to derive the objective and subjective weights of the considered evaluation criteria and DNMA is applied to prioritize urban climate change policy alternatives for transportation activities. Sensitivity and comparative analyses with existing studies confirm the proposed framework. The evaluation results show that the integration of transportation sectors, strategies, and innovations across different urban areas in all regions option has the highest overall utility degree (0.731) among a set of four urban climate change policy alternatives for transportation activities.

Shifting car travel to active modes to improve population health and achieve transport goals: A simulation study

IntroductionBeing physically active has multiple health benefits and contributes to the reduction of co-morbidities and mortality from chronic diseases. Active transport (walking and cycling) contributes to population health by enabling physical activity. We developed a simulation model to measure health impacts of transport scenarios for Melbourne, Australia. Our aim was to demonstrate active transport health impacts and support the materialization of policies for healthy cities and people. The model measures health impacts of increased physical activity from replacing short car trips for any purpose or for commuting under 5 km by walking and cycling. MethodsWe developed a micro-simulation model of physical activity and disease risk in combination with the well-established proportional multi-state life table model. We quantified life course health including health adjusted-life years, life years, new cases of diseases prevented, and deaths prevented for 14 chronic diseases associated with physical inactivity for the adult population of people from Melbourne, Australia in 2017. ResultsOver the life course of the Melbourne adult population of 3.6 million people in 2017, gains in health-adjusted life years ranged from 5,100 (95% Uncertainty Interval (UI) 3,700 to 6,500) for the scenario replacing commute trips by car under 1 km with walking up to 738,800 (95% UI 546,000 to 935,000) when replacing car trips under 2 km with walking and between 2 km and 5 km with cycling. We also estimated benefits in terms of reductions of new cases of diseases and deaths prevented, with the greatest gains for ischemic heart disease, stroke, Alzheimer's and other dementias and type 2 diabetes. ConclusionsWe found that shifting car travel to active modes would accrue important health benefits for the 2017 Melbourne population. Our results support policies and strategies for sustainable transport planning to contribute to reduce the burden from chronic diseases and environmental impact of car-oriented cities.