This paper introduces a novel optimisation algorithm for hybrid railway vehicles, combining a non‐linear programming solver with the highly efficient “Mayfly Algorithm” to address a non‐convex optimisation problem. The primary objective is to generate efficient trajectories that enable effective power distribution, optimal energy consumption, and economical use of multiple onboard power sources. By reducing unnecessary load stress on power sources during peak time, the algorithm contributes to lower maintenance costs, reduced downtime, and extended operational life of these sources. The algorithm's design considers various operational parameters, such as power demand, regenerative braking, velocity and additional power requirements, enabling it to optimise the energy consumption profile throughout the journey. Its adaptability to the unique characteristics of hybrid railway vehicles allows for efficient energy management by leveraging its hybrid powertrain capabilities.

The optimised trajectories demonstrate an average reduction of 16.85% in total energy consumption, showcasing the algorithm's effectiveness across diverse routes and conditions, with an average increase in journey times of only 0.40% and a 15.18% reduction in traction power. The algorithm achieves a well‐balanced energy‐time trade‐off, prioritising energy efficiency without significantly compromising journey duration. This balance is essential in sustainable transportation systems. Reducing energy consumption and emissions is vital without severely impacting service quality and travel times.image

The introduction of connected and automated vehicles (CAVs) has the potential to bring numerous advantages to urban mobility. However, many challenges for road infrastructure need to be overcome before those benefits can be achieved. This study addressed multiple dimensions of the implications of CAV deployment for road infrastructure through a comprehensive survey with 168 experts from different sectors and regions around the world. The issues are grouped into five categories: (1) key challenges of accommodating CAVs in existing urban transport networks; (2) infrastructure improvement required for shared CAV models; (3) maintenance aspects of infrastructure for CAVs; (4) implementation time of infrastructure support for CAVs; and (5) financing infrastructure upgrades to facilitate CAVs on the roads. The outcomes of the research show that there is still no consensus among the stakeholders on what should be considered to maximise CAV benefits for society as a whole. This indicates the necessity for cooperation between stakeholders to achieve the safe and efficient operation of CAVs. Overall, this study provides in‐depth insights for decision‐makers and transport planners to form policies, regulations, and guidelines regarding the future implementation of CAVs for roads before their commercialisation phase.

This study addressed multiple dimensions of the implications of connected and automated vehicle (CAV) deployment for road infrastructure through a comprehensive survey with 168 experts from different sectors and regions around the world. The issues are grouped into five categories: (1) key challenges of accommodating CAVs in existing urban transport networks; (2) infrastructure improvement required for shared CAV models; (3) maintenance aspects of infrastructure for CAVs; (4) implementation time of infrastructure support for CAVs; and (5) financing infrastructure upgrades to facilitate CAVs on the roads.image

Coordinated vehicle route guidance is recognized as an effective way to alleviate the Braess' paradox that new congestion is generated since numerous vehicles obey the same guidance from Google Maps. In conventional route games, decision‐makers are assumed to be completely rational (maximized utility is taken as an optimization objective). However, behavioral research finds that not only the expected profit is pursued, but also the fairness psychology is concerned by decision‐makers in game theory. In existing studies, the fairness concern of players is widely accepted in supply chains. Inspired by the fairness psychology of supply chain members, the vehicle fairness coefficient is originally proposed in this paper. Moreover, a unified fairness mechanism of the routing game is built to perfect the existing theoretical framework of route games. Specifically, the formulated vehicle fairness coefficient is incorporated into the payoff functions of a perfectly rational‐based routing game, which makes the single‐objective problem to be multiple. Compared with the current completely rational‐based route games, this improvement allows vehicles to focus not only on travel time but also on route schemes' fairness. The consideration of vehicle fairness concern leads the original symmetric payoff matrix of the route game to be asymmetric. Since the existing solving theorems are not necessarily valid for the asymmetrical multi‐player game, a matched reinforcement learning algorithm is adopted to solve this new game. An experimental study shows that the averaged fairness coefficient is increased from 0.798 to 1.00 on the basis of the equal travel time due to the working of the designed fairness mechanism.

Inspired by the fairness psychology of retailers in supply chains, this paper introduces the concept of fairness concern of drivers into the game theory‐based coordinated route guidance. A unified fairness mechanism of coordinated vehicle route guidance is originally built.image

Cooperative intelligent transport systems (C‐ITS) deployments for vehicle‐to‐infrastructure communication require substantial investments from European Member States in road‐side units (RSUs) and in central traffic management systems. The promise of numerous societal benefits should justify these public investments. However, C‐ITS uptake in passenger cars, and thus the subsequent societal benefits, are highly uncertain. Therefore, here, a case study of Flanders is presented in which real option analysis) is used to help road authorities assess the RSU investment opportunity. The framework combines a detailed cost and benefit model and includes managerial options for the road authority. This technique aims to incorporate the value of the flexibility that is available during the deployment to reduce risk exposure, and as such more accurately appraise the investment. C‐ITS uptake in passenger cars was modelled as the major source of uncertainty, as it is the primary driver of societal benefits. While a static RSU investment analysis for Flanders, Belgium, was found to be negative, embedding the option to defer the investment decision to further study C‐ITS uptake results in a positive average net present value. The results are useful for any road authority aspiring to roll out C‐ITS road‐side infrastructure.

Here, a real option analysis is presented that helps road authorities to assess the C‐ITS road‐side infrastructure business case. The work contains a case study of Flanders, Belgium, and combines a detailed cost and benefit model in combination with real options, in order to incorporate the value of the flexibility that road authorities have during the deployment to reduce risk exposure.image

Traffic congestion has been aggravated in many cities, which not only negatively affects the traffic efficiency, but also exacerbates the related social problems. Accurate prediction of urban road travel times plays a crucial role in assisting traffic management and alleviating the derived problems caused by traffic congestion. This study aims to propose an innovative Gated Recurrent Unit (GRU)‐based model for vehicle travel time prediction on urban road networks using the Internet vehicle travel time data. The Internet data used in the paper represents the real‐world average vehicle travel times on urban road networks, avoiding the privacy issue and the computational challenge in individual trajectory data. Before presenting the model, a data imputation method based on time series to reconstruct the missing data, and a time‐series data similarity evaluation method for road link classification are developed. The road‐category‐based model helps balance the computational efficiency and the prediction accuracy. Finally, the modelling framework is tested on a road network in Xuhui District, Shanghai. By comparing its performance with different models, it is concluded that the GRU‐based model considering road link categories is more efficient and more accurate in urban road travel time prediction.

This study aims to propose an innovative gated recurrent unit (GRU)‐based model for vehicle travel time prediction on urban road networks using the Internet vehicle travel time data. Before presenting the model, a data imputation method based on time series, a road link topology analysis to reconstruct the missing data, and a time‐series data similarity evaluation method for road link classification are developed. The road‐category‐based model helps balance the prediction accuracy and the computational cost.image