Transportation Engineering最新文献

The latest worldwide application of social distancing measures highlighted dramatic effects on the daily life, mobility, well-being, mental health, and economic activity of citizens during the pandemic. Unfortunately, societies were unprepared to confront a pandemic and applied movement restriction measures, often harsh and unbalanced, creating confusion, uncertainty, and annoyance to communities. In this paper, we reviewed a wide array of databases to categorise the mobility management measures implemented internationally in response to the social distancing restrictions that had to be taken during the first wave of the COVID-19 pandemic. Furthermore, to comprehensively assess the measures implemented to contain the COVID-19 pandemic from the perspective of the local community, we conducted focus group interviews with specific population groups that may have been affected to a greater extent. These groups consisted of vulnerable road users, such as mobility-impaired people and people who are facing severe health problems, representatives of the local economy and businesses and representatives of institutional stakeholders and policymakers. Our research showed that in the post-pandemic era, accessibility and not mobility should be at the heart of economic and social welfare. At the same time, the assurance of physical activity, which is strongly associated with the mental health of citizens, and the protection of the local economy are issues of outermost importance. However, local authorities were unprepared to manage these issues and often made fragmented and punitive decisions. By recognising the need for a regional or metropolitan emergency mechanism, this study aims to introduce a new system of indicators specifically adapted to the particularities and challenges encountered during the pandemic, accounts for local communities' unique needs and requirements, and complies with the general principles of Sustainable Urban Mobility Plans (SUMPs). In the future, the SUMP methodology should be revised to be able to handle and manage mobility systems under pandemic conditions.

In this paper, an integrated approach is proposed that incorporates the road network design problem and the vehicle routing problem, which are very often studied separately. This approach can be adopted when the routes to follow in a transport system have to be designed (e.g., for public transit with fixed and variable routes, or for freight distribution) jointly with the network topology and capacity. The goal is to minimise congestion and the impacts on the network from passengers and freight vehicles. The design (control) variables consist of the optimal vehicle routes and optimised road network; they are both discrete (link topology for passenger and freight, routes for passengers and freight vehicles, etc.) and continuous (link capacity in terms of reserved lanes, signal settings, etc.). The problem is modelled in congested transportation networks (e.g., in an urban centre). A heuristic algorithm is adopted to find the best network configuration and the best vehicle routes. The proposed model and the adopted algorithm are applied in a test system to analyse the limits of the methodology, to verify the advantage obtained from the joint method and its applicability. In the test system, the joint procedure gives good results in terms of cost decreases. The reported methods can help public and private stakeholders in making decisions regarding transportation policy, especially in the urban environment.

In this work, the new energy demand and greenhouse gas emissions that would be associated with heavy truck electrification in Ontario is evaluated. A new equation is derived to calculate the pollution-producing electricity generation based on the breakdown of the Ontario generation mosaic. Using this as a basis, for 4 scenarios of 5 %, 25 %, 50 % and 75 % of heavy-duty truck electrification, the Marginal Emission Factor (MEF) is calculated. This evaluation suggests that Ontario's peak demand in 2040 for the least and the most heavy-duty truck electrification scenarios can be up to 27.5 GW and 30.4 GW, respectively, compared to a baseline of 26.8 GW when assuming no heavy truck electrification condition. It is also forecasted that if the extra demand for electric trucks is supplied by clean renewable resources, the GHG emission reduction for 2040 can be as high as 0.9 MT, 4.3 MT, 8.6 MT and 12.9 MT GHG, respectively.

A fully integrated passenger multimodal system is a new concept in the transport industry, and consequently, data on system interoperability is lacking. In this paper, a qualitative research method is applied, to get the transport experts’ opinions and information on how the multimodal system should be designed. Although they are aware that the collaboration should result in an increase in the volume of passengers, the most challenging task is to convince transport operators to share the data with each other, due to legal issues. To determine the passengers’ perspective on making multimodal choices in their journeys, the travel demand model is developed, based on online survey data. The proposed (Binary Logistic Regression) model reveals the importance for policymakers and transport operators serving the airport to address access time reliability by carefully considering the passengers’ preferences in terms of their needs and age.

Walkability and equity in transport are crucial aspects of sustainable mobility and social well-being. The x-minute city concept emphasizes the importance of walkability, by fostering the design of a city where people can easily access their daily needs aiming to reduce reliance on private cars. However, such approaches, which generally rely on the idea of an “average resident” can ignore inequalities amongst people and fail to achieve the goal of building urban environments where everyone can participate in city life regardless of their socio-economic characteristics and vulnerability. In this study we propose an approach to assess the equity of the x-minute city, highlighting the limitations of the current application of the concept. The approach includes the computation of x-minute thresholds based on the walkability of pedestrian paths and considering different users’ needs. Home to school trips and social trips are taken as a reference; equity metrics such as the Lorenz Curve and Gini Index are used to assess how the x-minute city concurs with the transport equity of a city. The results of the assessment can help identify potential disparities in access to key destinations among different user groups, and support evidence-based policy recommendations to promote equitable transportation options. The case study of Bari, Italy, is used to illustrate the application of the method; however, the proposed approach can be replicated in different contexts, contributing to the ongoing discourse on walkability and equity in transport.

Pavement performance prediction is crucial for ensuring the longevity and safety of road networks. In our extensive study, we employ a diverse array of techniques to enhance fatigue performance models in flexible pavements. The methodology begins with Random Forest feature selection, identifying the top 15 critical variables that significantly impact pavement performance. These variables form the basis for subsequent model development. Our investigation into model performance indicates the superiority of advanced machine learning methods such as Regression Trees (RT), Gaussian Process Regression (GPR), Support Vector Machines (SVM), Ensemble Trees (ET), and Artificial Neural Networks (ANN) over traditional linear regression methods. This consistent outperformance underscores their potential to reshape forecasting accuracy. Through extensive model optimization, we reveal robust performance across both complete and selected feature sets, emphasizing the importance of meticulous feature selection in enhancing forecast accuracy. The accuracy of our best optimized machine learning model is highlighted by its Performance Measurement metrics: RMSE of 22.416, MSE of 502.46, R-squared of 0.80848, and MAE of 8.9958. Additionally, comparative analysis with previous empirical models demonstrates that our best optimized machine learning model outperforms existing empirical models. This work underscores the significance of feature curation in pavement performance prediction, highlighting the potential of sophisticated modeling methodologies. Embracing cutting-edge technologies facilitates data-driven decisions, ultimately contributing to the development of more robust road networks, enhancing safety, and prolonging lifespan.

Passenger alighting estimation is a critical task in public transport (PT) management, especially for entry-only Automatic Fare Collection (AFC) transport systems where passenger alighting are not recorded. Effective estimation methods are necessary for trip analysis and route planning, offering valuable insights into passengers’ mobility patterns and, subsequently, improving the quality of service. However, the stochastic nature of passenger behaviour challenges the degree of successful alighting estimates. A classic approach to infer the alighting stops of passengers is the use of trip-chaining principles. Since these principles are dispersed across the literature in the field, their comprehensive review is pivotal to establish the best practice for alighting estimation. Still, trip-chaining approaches are unable to infer the alighting of non-commuting passengers. This paper addresses these two research gaps by: i) providing a critical overview of the existing principles and methods for alighting estimation; ii) proposing an approach to improve alighting estimation that consistently integrates the most effective state-of-the-art principles on trip-chaining; and iii) further introducing a frequent pattern mining and density-based clustering solutions to support alighting estimation for non-commuting passengers. Considering the public bus transport in Lisbon city as the guiding case study, the achieved estimation rate by the proposed assembled model is 92%. Moreover, the density-based clustering solution is found to improve the estimation of 11pp against classic trip-chaining principles. Furthermore, the proposed model and acquired results yield actionable value to enhance PT operations and services, ultimately leading to improved bus routing and quality of service.

In recent years, Machine Learning (ML) techniques have gained increasing popularity in several fields thanks to their ability to find hidden and complex relationships between data. Their capabilities for solving complex optimization tasks have made them extremely attractive also for the design of the Energy Management System (EMS) of electrified vehicles. Among the plethora of existing techniques, Reinforcement Learning (RL) algorithms have unprecedented potential since they can self-learn by directly interacting with the external environment through a trial-and-error procedure. In this paper, a Deep Q-Learning (DQL) agent, which exploits Deep Neural Networks (DNNs) to map the state-action pair to its value, was trained to reduce the CO₂ emissions of a state-of-the-art diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. The proposed methodology was tested on a virtual test rig of the investigated vehicle while operating on a charge-sustaining logic. A sensitivity analysis was performed on the reward to test the capabilities of different penalty functions to improve the fuel economy while guaranteeing the battery charge sustainability. The potential of the proposed control strategy was firstly assessed on the Worldwide harmonized Light-duty vehicles Test Cycle (WLTC) and benchmarked against a Dynamic Programming (DP) optimization to evaluate each reward. Then the best agent was tested on a wide range of type-approval and Read Driving Emission (RDE) scenarios. The results show that the best-performing agent can reach performance close to the DP reference, with a limited gap (7 %) in terms of CO₂ emissions.

Reclaimed asphalt pavements (RA) usually are fully recyclable in new asphalt mixtures. The properties of the individually aged and possibly modified bitumen in the RA will affect the mix design of the new asphalt mixture. Furthermore, the RA may contain hazardous PAH-contamination resulting from coal tar, which was applied in German road pavements up to the 1980′s. For identifying substances that impair or influence reusability of the RA the Fourier transform infrared spectroscopy (FTIR) method was modified. Within a total measuring time of approx. 20 min, the binder contained in a sample of granulated road construction material can be recovered by means of a rapid extraction and measured by FTIR-ATR. By evaluating the measured absorption spectra, samples which have to be considered as PAH-contaminated (with PAH contents ≥ 25 mg/kg) could be distinguished from non-contaminated reclaimed asphalt. Furthermore, the presence of styrene-butadiene-styrene (SBS) modification as well as viscosity-changing organic additives could be identified.

This study focuses on the strategic decision-making process for selecting airport locations in developing countries, where investment constraint is a significant concern. Recognizing that airport location decisions are influenced by a multitude of factors, often in the absence of comprehensive data and amidst uncertain forecasts, there is a need for a methodology capable of adeptly handling various factors under uncertainty. The goal is to provide solutions that remain feasible and near-optimal, even with some variations in input parameters, ensuring robustness. To this end, this research introduces a decision tool incorporating Slack-Based Measurement in Robust Data Envelopment Analysis (SBM-RDEA). The Slack-based method is beneficial in cases with multiple inputs and outputs where their relationships are not strictly proportional, as in the airport location selection problem. DEA, in conjunction with Multiple Regression analysis, leverages available data on the attributes of operational regional airports to compute the significance of these criteria in shaping the success factors of these facilities. Furthermore, these weightings can be employed to evaluate the suitability of airport locations currently under development and assess their potential for contributing to regional development. Additionally, to address the uncertainty of input data, a Robust optimization approach is employed to ensure a reliable response for this strategic decision-making process. This weighting framework is utilized twice within the research methodology: first, to assess the potential of different counties in the state for hosting a new regional airport, and second, to identify the optimal location for the airport in an MCDM-GIS analysis. This methodology has been applied to a case study in Sistan and Baluchistan, Iran. The result of the proposed method has been compared with one of the most common existing methodologies. This method identifies Zahedan County as having the highest potential and selects the location (29.48° N, 60.90° E), in the east of Zahedan city, as the optimal site for airport construction. Validation results confirm the efficacy of this solution.