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The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented.

First, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented.

Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments.

The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains.

The serious wear problem of heavy-haul freight vehicle wheels affects the safety and economy of vehicle operation. This paper aims to study wheel wear evolution law and the influence of line parameters on wheel wear of heavy-haul freight, and provide the basis for operation and line maintenance.

The wheel wear test data of heavy-haul freight vehicles were analyzed. Then a heavy-haul freight vehicle dynamic model was established. The line parameters influencing wheel wear in heavy-haul freight vehicles were also analyzed by the Jendel wear model, and the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear were analyzed.

A rail cant of 1:40 results in less wheel wear; an increase in the rail gauge can reduce wheel wear; and when matched with the CHN60 rail, the wear depth is relatively small. A decrease of 9.21% in wheel wear depth when matched with the CHN60 rail profile. The ramp of the heavy-haul line is necessary to consider for calculating wheel wear. When the ramp is considered, the wear depth increases by 8.47%. The larger the ramp, the greater the braking force and therefore, the greater of the wheel wear.

This paper first summarizes the wear characteristics of wheels in heavy-haul freight vehicles and then systematically analyzes the effect of line parameters on wheel wear. In particular, this study researched the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0038/

To assess the introduction and performance of light electric freight vehicles (LEFVs), more specifically cargo cycles in major 3PL organizations in at least two Nordic countries.

Case studies. Interviews. Company data on performance before as well as after the introduction. Study of differing business models as well as operational setups.

The results from the studied cases show that LEFVs can compete with conventional vans in last mile delivery operations of e-commerce parcels. We account for when this might be the case, during which circumstances and why.

Inherent limitations of the case study approach, specifically on generalization. Future research to include more public–private partnership and multi-actor approach for scalability.

Adding to knowledge on the public sector facilitation necessary to succeed with implementation and identifying cases in which LEFVs might offer efficiency gains over more traditional delivery vehicles.

One novelty is the access to detailed data from before the implementation of new vehicles and the data after the implementation. A fair comparison is made possible by the operational structure, area of delivery, number of customers, customer density, type of packages, and to some extent, the number of packages being quite similar. Additionally, we provide data showing how city hubs can allow cargo cycles to work synergistically with delivery vans. This is valuable information for organizations thinking of trying LEFVs in operations as well as municipalities/local authorities that are interested.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

As of December 2022, there were 119,000 gas stations, 10,800 gas stations and 4,488,000 charging piles nationwide, while the number of vehicles reached 312 million, including 11.49 million new energy vehicles. The imbalance between transportation energy supply and energy replenishment demand leads to crowded queues of vehicles at some stations and idle resources in others. How to reduce the phenomenon of large queues and improve the utilization rate of idle resources is the key to alleviating the imbalance between supply and demand.

Therefore, from the perspective of spatio-temporal equilibrium of urban transportation energy supply stations, multi-energy supply station cooperation is established in view of the phenomenon of large spatio-temporal differences among different energy supply stations, and corresponding inducing strategies are adopted for energy supplement vehicles in the road network, so that part of queued users go to energy supply stations with fewer vehicles, so as to balance the supply and demand of transportation energy in the region. On this basis, the income distribution of urban transportation energy supply station is discussed.

The total revenue after the cooperation was 13,095, an increase of 22.9%. Secondly, in terms of distribution rationality, three impact factors are selected and Shapley correction value is used to distribute the total income. Compared with independent operation, both sites have a certain degree of increase.

Traffic congestion at energy supply stations is closely related to the number, location and number of vehicles at energy supply stations. Therefore, using a cooperative approach of energy trading cannot solve the queuing problem. In addition, there are a few research results on the equalization of energy supply station services considering time-of-use pricing. However, these studies do not consider the vehicular grooming at congested stations. As far as the authors know, there are no relevant research results in the research on the service equilibrium of energy supply stations based on cooperative games.

Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.

In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.

A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.

This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

The purpose of this study is to propose a precise and standardized strategy for numerically simulating vehicle aerodynamics.

Error sources in computational fluid dynamics were analyzed. Additionally, controllable experiential and discretization errors, which significantly influence the calculated results, are expounded upon. Considering the airflow mechanism around a vehicle, the computational efficiency and accuracy of each solution strategy were compared and analyzed through numerous computational cases. Finally, the most suitable numerical strategy, including the turbulence model, simplified vehicle model, calculation domain, boundary conditions, grids and discretization scheme, was identified. Two simplified vehicle models were introduced, and relevant wind tunnel tests were performed to validate the selected strategy.

Errors in vehicle computational aerodynamics mainly stem from the unreasonable simplification of the vehicle model, calculation domain, definite solution conditions, grid strategy and discretization schemes. Using the proposed standardized numerical strategy, the simulated steady and transient aerodynamic characteristics agreed well with the experimental results.

Building upon the modified Low-Reynolds Number k-e model and Scale Adaptive Simulation model, to the best of the authors’ knowledge, a precise and standardized numerical simulation strategy for vehicle aerodynamics is proposed for the first time, which can be integrated into vehicle research and design.

This study aims to examine the differential impact of ride-hailing services (RHS) on private and commercial vehicle ownership from five metropolitan cities in India.

Using vehicle ownership data from five metropolitan cities over period 1991 to 2020, a panel corrected standard errors model was estimated to model the association between RHS and vehicle ownership.

The results indicate that advent of RHS has led to a significant reduction in private vehicle ownership rates and a corresponding increase in addition of intermediate public transport. The net effects of RHS on road congestion and pollution levels need to be studied in detail.

The findings of this study can potentially assist policymakers and mobility planners in efforts to decarbonise and decongest urban transport.

This study sets precedence in analysing the impact of RHS on private and commercial vehicle independently. Further, to the best of the author’s knowledge, this is the first study to examine this association for the city of Delhi and Kolkata.

The case study has been prepared for management students/business executives to understand electric vehicle (EV) business, business environment, industry competition and strategic planning and strategy implementation.

The size of the Indian passenger vehicle market was valued at US$32.70bn in 2021; it was projected to touch US$54.84bn by 2027 with a Compound Annual Growth Rate (CAGR) of more than 9% during the period 2022–2027. The passenger vehicle industry, a part of the overall automotive industry, was expected to grow at a rapid pace, as the Indian economy was rising at the fastest rate. However, the Government of India (GoI) had put a condition on the growth scenario by mandating that 100% of vehicles produced would be EVs by 2030. Tata Motors (TaMo), a domestic player in the market, had been facing a challenging competitive environment. Although it had been incurring losses, it had successfully ventured into the EV business. TaMo had taken advantage of the first mover by creating an electric mobility business vertical to enable the company to deliver on its aspiration of providing innovative and competitive e-mobility solutions. TaMo leadership had been putting efforts to scale up the electric mobility business, thus, contributing to GoI’s plan for electric mobility. Shailesh Chandra, president of electric mobility business, had a big task in hand. He had to scale up EV production and sales despite insufficient infrastructure for charging and shortages of electronic components for manufacturing.

The case study has been prepared for management students/business executives for strategic management class. It is recommended that the case study is distributed in advance so that the students can prepare well in advance for classroom discussions. Groups will be created to delve into details for a specific question. While one group will make their presentation, the other groups will question the solution provided and give suggestions.

Teaching notes are available for educators only.

CSS 11: Strategy.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.