Exploring sustainable pathways for urban traffic decarbonization: vehicle technologies, management strategies, and driving behaviour

The global fight against climate change and air pollution prioritizes the transition to sustainable transportation options. Understanding the impacts of various sustainable pathways on emissions, travel time, and costs is crucial for researchers and policymakers. This research conducts a comprehensive microsimulation of traffic and emissions in downtown Toronto, Canada, to examine decarbonization scenarios. The resulting 140 scenarios involve different fuel types, Connected and Automated Vehicles (CAV) penetration rates, and routing strategies combined with driving style. To achieve this, transformers-based prediction models accurately forecast Greenhouse Gas (GHG) and Nitrogen Oxides (NOx) emissions and average speed for eco-routing. The study finds that 100% battery electric vehicles have the lowest GHG emissions, showing their potential as a sustainable transportation solution. However, challenges related to cost and availability persist. Hybrid Electric Vehicles and e-fuels demonstrate considerable emission reductions, emerging as promising alternatives. Integrating CAVs with anticipatory routing strategies significantly reduces GHG emissions. Additionally, eco-driving practices and eco-routing strategies have a notable impact on NOx emissions and travel time. Comprehensive cost analysis provides valuable insights into the economic implications of various strategies and technologies. These findings offer guidance to various stakeholders in formulating effective strategies, behaviour changes, and policies for emission reduction and sustainable transportation development

Scheduled service network design with synchronization and transshipment constraints for intermodal container transportation networks

In this paper we address the problem of scheduled service network design for container freight distribution along rivers, canals, and coastlines. We propose a new concise continuous- time mixed-integer linear programming model that accurately evaluates the time of occurrence of transportation events and the number of containers transshipped between vehicles. Given the transportation network, the eet of available vehicles, the demand and the supply of containers, the sailing time of vehicles, and the structure of costs, the objective of the model is to build a minimum cost service network design and container distribution plan that denes services, their departure and arrival times, as well as vehicle and container routing. The model is solved with a commercial solver and is tested on data instances inspired from real-world problems encountered by EU carrier companies. The results of the computational study show that in scheduled service networks direct routes happen more often when either the eet capacity is tight or the handling costs and the lead time interval increase. The increase of the same parameters leads to the decrease of the number of containers transshipped between vehicles

A multimodal network flow problem with product quality preservation, transshipment, and asset management

In this paper, we present an optimization model for a transportation planning problem with multiple transportation modes, highly perishable products, demand and supply dynamics, and management of the reusable transport units (RTIs). Such a problem arises in the European horticultural chain, for example. As a result of geographic dispersion of production and market, a reliable transportation solutions ensures long-term success in the European market. The model is an extension to the network ow problem. We integrate dynamic allocation, ow, and repositioning of the RTIs in order to nd the trade-o between quality requirements and operational considerations and costs. We also present detailed computational results and analysis

The state of the art of cooperative and connected autonomous vehicles from the future mobility management perspective:a systematic review

© 2022 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/futuretransp2030032Cooperative and connected autonomous vehicles (CCAVs) are considered to be a promising solution for addressing congestion and other operational deficiencies, as part of a holistic future mobility management framework. As a result, a significant number of studies have recently been published on this topic. From the perspective of future mobility management, this review paper discusses three themes, which are traffic management, network performance, and mobility management, including congestion, and incident detection using the PRISMA methodology. Three databases were considered for this study, and peer-reviewed primary studies were selected that were published within the last 10 years in the English language, focusing on CCAV in the context of the future transportation and mobility management perspective. For synthesis and interpretation, like-for-like comparisons were made among studies; it was found that extensive research-supported information is required to ensure a smooth transition from conventional vehicles to the CCAVs regime, to achieve the projected traffic and environmental benefits. Research investigations are ongoing to optimize these benefits and associated goals via the setting of different models and simulations. The tools and technologies for the testing and simulation of CCAV were found to have limited capacity. Following the review of the current state-of-the-art, recommendations for future research have been discussed. The most notable is the need for large-scale simulations to understand the impact of CCAVs beyond corridor-based and small-scale networks, the need for understanding the interactions between the drivers of CCAVs and traffic management centers, and the need to assess the technological transition, as far as infrastructure systems are concerned, that is necessary for the progressive penetration of CCAVs into traffic streams.This research was funded by European Union’s Horizon 2020 research and innovation program, grant number 955317.Published onlin

Integrating passenger and freight transportation : model formulation and insights

Integrating passenger and freight flows creates attractive business opportunities because the same transportation needs can be met with fewer vehicles and emissions. This paper seeks an integrated solution for the transportation of passenger and freight simultaneously, so that fewer vehicles are required. The newly introduced problem concerns scheduling a set of vehicles to serve the requests such that a part of the journey can be carried out on a scheduled passenger transportation service. We propose an arc-based mixed integer programming formulation for the integrated transportation system. Computational results on a set of instances provide a clear understanding on the benefits of integrating passenger and freight transportation in the current networks, considering multi-modality of traditional passenger-oriented transportation modes, such as taxi, bus, train or tram