Luca Persia

Background: City freight logistics are increasingly supported by cyber-physical and digital-twin approaches, yet many frameworks are evaluated primarily using simulated data. This study empirically assesses selected PANDORAS functions using large-scale industrial logistics records. Methods: Historical delivery sequences and coordinates from 126,073 delivery operations were used to reconstruct route–day operations and calculate inter-stop geodesic distance proxies. Product-weight data from 4439 SKU records were used to construct deterministic delivery-level load proxies and a comparative load-sensitive transport-intensity indicator. A deterministic within-route spatial reordering procedure was applied while preserving route–day membership. Results: Reordering reduced the total inter-stop geodesic distance proxy from 18,420 km to 15,870 km, corresponding to a 13.84% reduction. Load-sensitive assessment additionally captured differences in inferred carried-load profiles that are not represented by distance alone. Conclusions: Industrial logistics data can support a transparent and reproducible empirical implementation of selected PANDORAS functions. The environmental indicators should be interpreted as comparative proxies rather than measured energy consumption or emissions. Further validation requires road-network data, observed loads, vehicle characteristics, and operational telemetry.

This chapter provides a comprehensive overview of sustainable mobility as a cornerstone of contemporary transport planning, with particular emphasis on urban contexts. It examines how growing and increasingly unpredictable mobility demand, urban sprawl, congestion, pollution, road safety issues, and social exclusion challenge the sustainability of transport systems. Framed within the United Nations 2030 Agenda, the chapter introduces the environmental, social, and economic dimensions of sustainable mobility and discusses key definitions developed by international institutions. It presents performance indicators used to measure transport sustainability and compares mobility patterns across Italian, European, and global cities. The chapter reviews policy levers for improving sustainability, including land-use planning, pricing measures, and transport demand management, highlighting the importance of integrated policy packages. Emerging trends such as electric mobility, shared mobility, and connected and automated transport are analyzed through a life cycle and system perspective. Finally, the chapter introduces planning tools such as sustainable urban mobility plans and illustrates their application through real-world case studies.

The concept of sustainability and sustainable development has evolved over several decades through a series of key international milestones. One of the earliest and most significant steps was the United Nations Conference on the Human Environment, held in Stockholm in 1972, which established fundamental principles concerning human rights and responsibilities toward the environment ( UN, 1972 ).

Sustainability for Engineering and Science provides a comprehensive overview of sustainability issues related to the built environment and in line with the UN's

Real-time crash risk assessment is a key component of proactive road safety management, enabling the identification of hazardous conditions within short temporal intervals before crashes occur. Traditional crash-based models are unsuitable for such applications due to the rarity, reporting delay, and stochastic nature of crash data. Traffic conflicts, capturing near-miss interactions between road users, provide a practical alternative for real-time safety analysis. Over the past decade, numerous modelling approaches have been developed to translate conflict information into crash risk estimates; however, the literature remains fragmented and lacks a unified analytical synthesis. This review presents a state-of-the-art, model-centric analysis of conflict-based approaches, classifying them into five paradigms: statistical/regression-based, Bayesian, extreme value theory (EVT), machine learning (ML), and hybrid models. Beyond classification, the study conducts a structured cross-paradigm comparison across key dimensions, including conflict representation, data characteristics, temporal modelling, uncertainty treatment, validation strategies, computational complexity, and operational readiness. The paradigms are further interpreted through the complementary lenses of conflict frequency and severity. The review identifies key research gaps, including fragmented conflict definitions, challenges in modelling rare and extreme events, incomplete treatment of uncertainty and spatiotemporal dynamics, and limitations in validation, transferability, and deployment. Emerging research directions include standardized and adaptive conflict indicators, EVT–machine learning integration, integrated uncertainty-aware frameworks, advanced spatiotemporal modelling, transferable models, and scalable real-time implementation. By combining structured evidence mapping and cross-paradigm synthesis, this study supports model selection, development, and deployment for dynamic crash risk assessment.

The importance of the infrastructure is associated with the value of the infrastructure, the greater the importance of infrastructure, the greater its value. The concept of the importance of road infrastructure can take on a different value instead of different points of view. For example, roads can be evaluated from an economic, social, political, and military, among others. In 2021, the Lazio Regional Road Authority (ASTRAL) requested assistance from the Research Center for Transport and Logistics (CTL) to develop a composite scoring index (Regional Index, Ri) that would rank the relative importance of ASTRAL–maintained roadway network. The Ri index is expressed numerically between values from 1 to 5 (with 5 representing the highest importance). It includes the following variables: Population density, AADT, road traffic crashes, accessibility to point of interest, maintenance cost, air emissions, and noise pollution. The methodology includes the following steps. First, the variables were selected on the basis of their reliability, measurability, coverage and relevance to the phenomenon to be measured. Then, the data collection and normalization of the variables on a scale of 1 to 5 were carried out. Subsequently, through a multicriteria analysis, the variables were weighted and added. Finally, a sensitivity analysis was performed to evaluate which variables had the most influence on the final output of the formula. The methodology proposed has been implemented on the Region Lazio roadway network in order to obtain the Ri of the road segments.

Road traffic crashes claim approximately 1.19 million lives annually worldwide, with low- and middle-income countries (LMICs) bearing a disproportionately high share of this burden. Intersections in these contexts are particularly hazardous due to mixed, non-lane-based traffic and infrastructural constraints. This study analysed 1242 police-reported intersection crashes (2021–2025) from Douala and Yaoundé, Cameroon, using binary probit and logistic regression models to identify infrastructural and environmental determinants of crash severity. Results from both models were consistent, indicating that late-night and early-morning crashes (00:00–05:59) significantly increased the probability of severe outcomes by 13.5% (p-value < 0.05), while single-lane roads raised it by 21.5% (p-value < 0.05; OR = 5.38), and two-lane roads raised the probability by 9.1% (p-value < 0.05; OR = 3.90) compared with multilane sections. Additionally, centre lines were associated with safer outcomes than physical separation (p-value < 0.05; OR = 0.30). Although model fit indices were modest (Nagelkerke R² = 0.118), typical of cross-sectional crash-severity models, the findings underscore the dominant influence of road geometry and lighting-related temporal exposure in shaping intersection crash outcomes. These insights provide a basis for targeted interventions such as road widening, improved night-time illumination, and simplified midblock designs to enhance safety in Cameroon and similar LMIC urban settings.