Search Results (358)

Background: Port access traffic combines pronounced calendar structure with substantial day-to-day variability, raising the question of whether temporal heterogeneity should condition a common forecasting configuration or support model specialization. Methods: Daily car and freight-truck arrivals at an urban non-containerized port were forecast at 1-, 7-, and 14-day horizons using statistical, machine-learning, and composite models under a leakage-free rolling-origin protocol. A unified calendar-conditioned ensemble was compared with a prespecified regime-based model assignment, with development, test, and temporal stress-test periods kept distinct. Results: The unified ensemble achieved next-day R² values of 0.75 for trucks and 0.80 for cars on the held-out 2018 test period and 0.73 and 0.75, respectively, under the 2019 temporal stress-test. Model preference varied jointly with temporal regime, forecast horizon, and evaluation period. Although fixed assignment improved car accuracy on the 2018 test period, the model–regime dominance required to justify it was not sufficiently stable across horizons and evaluation periods. Conclusions: Calendar conditioning provides a robust strategy for the present forecasting problem. More generally, fixed regime-based specialization should be supported by sufficiently stable model–regime dominance rather than inferred from identifiable temporal heterogeneity alone. Full article

Sustainable urban freight logistics requires routing decisions that jointly account for operating cost, vehicle technology, low-emission-zone (LEZ) access, terrain-sensitive energy use, and battery feasibility. This study develops a policy–terrain-coupled mixed-fleet routing framework integrating LEZ exposure, system-level carbon settlement, terrain-sensitive energy consumption, electric-vehicle (EV) battery feasibility, and route-level EV/internal-combustion-engine vehicle reassignment within a unified daily total operational cost (DTOC) evaluator. An adaptive large-neighborhood search (ALNS) procedure reconstructs feasible routes, while vehicle type is re-evaluated through counterfactual comparison of the complete system objective. The main experiments use 60 enhanced Gehring–Homberger benchmark-derived scenarios and 20 independent seeds, supplemented by ablation, carbon-price, EV-fixed-cost, heuristic-weight, convergence, and customer-scale scalability analyses. The ALNS-based framework achieves the lowest mean DTOC among the tested procedures, albeit with higher runtime. Policy and terrain information alter modeled fleet composition, with topology-dependent cost effects. Lower EV fixed costs consistently increase EV share, whereas carbon-price effects vary across network structures. All runs in the additional 200–1000-customer tests were feasible, although runtime increased with problem size. London- and Madrid-informed cases are treated as archetypes rather than as real-world validation cases. These results provide a basis for scenario screening and comparative planning of policy–terrain interactions before city-specific calibration and deployment. Full article

Urban freight plays a vital role in supporting India’s growing cities, which contribute over 58% of the national GDP. Bengaluru, a key logistics hub, faces increasing freight-related pollution, with freight vehicles accounting for nearly 50% of the city’s PM2.5 emissions. While E-LCV adoption is gaining attention, most studies focus on economic and technical factors, neglecting drivers’ symbolic and identity-based motivations. This study examines the association of symbolic perceptions, perceived social status, and reported motivations with freight drivers’ current E-LCV and ICE-LCV use in Bengaluru. A field survey of 112 drivers (54 E-LCV and 58 ICE-LCV) was conducted. The variables examined included pride, symbolic meaning, educational attainment, reported information sources, peer-related perceptions, and reported motivations. Because conventional maximum-likelihood logistic regression encountered complete or quasi-complete separation, Firth penalised logistic regression and bivariate comparative tests were used alongside descriptive analysis. Clear descriptive differences were observed between E-LCV and ICE-LCV drivers in perceived pride, social status, education profiles, media exposure, and reported motivations. Current E-LCV drivers reported substantially stronger pride and status-related perceptions associated with E-LCV use. However, given the cross-sectional design, these associations should not be interpreted as causal determinants of adoption. Differences were also observed in educational attainment and reported information sources between the two driver groups. The findings highlight the value of considering drivers as stakeholders with identity-related perceptions rather than solely as vehicle users. Understanding the social and symbolic meanings attached to E-LCVs may provide useful insights for future research and policy development concerning urban freight electrification. Recognising drivers not only as vehicle users but also as stakeholders with identity-related motivations may help inform more targeted policies and outreach strategies in cities such as Bengaluru. Full article

Last-mile delivery is constrained by manual cargo handling, which consumes a large share of the operating window and limits the number of delivery rounds per shift. This study evaluates the operational and economic effects of the Automated Horizontal Loading and Unloading System (AHLUS), a retrofittable in-vehicle technology that converts the cargo bed into an active handling platform using a belt conveyor and a movable bulkhead. Using daily records from two AHLUS-equipped one-ton trucks operated in a South Korean fresh-food network over four months, an interrupted time-series regression with Newey-West standard errors estimated the effect of the deployment process—encompassing driver adaptation and the dispatch reallocation enabled by the system’s enhanced handling capacity—while controlling for the pre-intervention trend. After stabilization, daily throughput was approximately 19.8 percent higher than the learning-phase baseline (approximately 14.8 percent relative to the model-implied counterfactual trend), a gain that was statistically significant, held for both drivers, and corresponded to an increase from two to three delivery rounds per shift. A transparent total cost of ownership model incorporating payload-loss, power, maintenance, and downtime yielded a base-case break-even point of 4.1 months, and a Monte Carlo simulation indicated a positive net benefit across all sampled parameter combinations under the assumed input distributions, with a median break-even point of 4.6 months. As an exploratory single-company field validation without an untreated control series, the study estimates the effect of the deployment as implemented in practice rather than the isolated effect of the hardware. The findings provide field-based evidence that in-vehicle handling automation can deliver measurable throughput and economic benefits in last-mile operations, pending confirmation in larger multi-company and multi-region deployments. Full article

With the continued electrification and digitalization of urban logistics, electric freight routing increasingly requires the coordinated consideration of customer time windows, vehicle capacity, limited battery range, and en-route charging. This study formulates an electric vehicle routing problem with time windows (EVRPTW) for smart-city electric freight and develops a multi-strategy improved ant colony optimization algorithm (IACO). The proposed model integrates customer service, route continuity, time windows, vehicle capacity, battery-energy propagation, and en-route charging. IACO combines a route–charging-state representation with feasibility-guided sweep-insertion initialization, max–min pheromone control, multi-representative guidance, reachable charging-station insertion, greedy feasibility repair, and 2-opt local search, forming a multi-stage search process that integrates global exploration, feasibility restoration, and local intensification. Computational experiments on an R-C benchmark scenario with 51 customers and 9 charging stations compare IACO with ACO, GA, TS, LNS, SA, PSO, and WOA over 100 independent runs under a common 300-iteration limit. Under the current experimental protocol, IACO records a representative generalized cost of 570.88, with reductions of 7.75–44.65% relative to the seven comparison methods, while its median CPU time is 28.42 s. These results demonstrate a clear solution-quality–computation trade-off and indicate the potential of IACO for plan-level electric freight routing and en-route charging coordination. Full article

Air pollution in urban and coastal regions is increasingly shaped by interactions among port operations, freight logistics, urban mobility systems, spatial development patterns, and governance arrangements. Although emission reduction technologies and regulatory measures have advanced significantly, their implementation frequently remains distributed across sector-specific institutional and operational domains. This paper introduces the concept of Air-Aware Port–City–Logistics Systems, proposing an integrated governance framework that treats air quality as a system-level outcome of linked maritime, logistics, urban transport, spatial, technological, and adaptive processes. Drawing on a structured interdisciplinary synthesis of literature in transport planning, maritime economics, logistics, environmental governance, air-quality management, and resilience, the study identifies key gaps in cross-sectoral coordination and in the governance of interdependencies influencing air-quality outcomes. The framework maps relationships among emission sources, transport and logistics flows, spatial configurations, population exposure, institutional coordination, technological and data systems, and adaptive capacity, thereby identifying potential intervention points across the port–city–logistics system. Its distinctive contribution lies not merely in combining these dimensions, but in organising them through a governance-oriented architecture in which monitoring, operational decisions, institutional responses, and adaptive learning are treated as interconnected processes. The framework also incorporates resilience and risk perspectives, emphasising adaptive governance approaches capable of responding to disruptions, changing demand patterns, climatic pressures, and other external environmental influences. By advancing a systems-based perspective, the paper contributes to air quality management and policy development through the integration of port–city interfaces, freight distribution networks, mobility planning, exposure considerations, and institutional decision-making within a unified analytical framework. As a conceptual framework, AAPCLS provides a transferable basis for future empirical application, contextual adaptation, and policy-oriented analysis rather than a validated operational model. Full article

Historic urban manufacturing territories are increasingly being reconsidered as strategic components of metropolitan sustainability rather than obsolete remnants of industrial economies slated for redevelopment. While these districts have long been associated with pollution, environmental degradation, and post-industrial decline, growing interest in circular economy strategies, Urban Resource Recovery (URR), and climate adaptation has renewed attention to their territorial significance. Using the North Brooklyn Industrial Business Zone and the Newtown Creek watershed as a case study, this paper argues that certain historic manufacturing territories possess an inherent capacity to evolve into Metropolitan Metabolic Infrastructure because of inherited geographic, infrastructural, and institutional characteristics, including waterfront access, freight networks, industrial zoning, large industrial parcels, and utility systems. Drawing upon research developed through the New York City Department of Design and Construction’s Town+Gown Urban Resource Recovery Working Group, the study combines historical analysis, spatial interpretation, planning policy, and case-study research to examine the evolving relationship among urban metabolism, circular economy, environmental justice, and Healthy Cities. Rather than advocating industrial preservation irrespective of environmental performance, the paper argues that these territories can support metropolitan material circulation while advancing cleaner industrial practices, environmental remediation, and more equitable urban development. The Metropolitan Metabolic Infrastructure framework offers a planning perspective for understanding how inherited manufacturing landscapes may contribute to resilient, circular, and healthier metropolitan regions while informing future comparative research on industrial territories undergoing similar transitions worldwide. Full article

The rapid growth of e-commerce has intensified last-mile delivery activities in urban areas, creating challenges for city logistics systems related to operational efficiency, congestion, and environmental impacts. In this context, understanding the factors that influence customer satisfaction with logistics operators is increasingly important, as mismatches between customer expectations and delivery service characteristics may lead to operational inefficiencies such as failed delivery attempts and repeated delivery rounds. This study proposes a machine learning framework for predicting customer satisfaction with postal and logistics operators in urban delivery systems using survey data on customer characteristics, preferences, and service perceptions. Several machine learning algorithms were developed and evaluated to identify the key determinants of customer satisfaction and assess their predictive performance. Beyond predictive accuracy, the study interprets customer satisfaction as an indicator of the alignment between customer expectations and delivery service configurations. Improved alignment may support service configurations that reduce delivery mismatches and repeated delivery attempts, which are recognized as a significant source of additional transport activity in urban freight systems. By identifying customer segments whose expectations are not adequately addressed by existing delivery services, the proposed framework can support more informed service design and operational decision-making. From a city logistics perspective, the potential reduction in failed deliveries and repeated delivery rounds may contribute to lower vehicle kilometers travelled, congestion, energy consumption, and emissions associated with urban freight transport, although these operational and environmental indicators were not directly measured in this study. The proposed approach therefore provides a data-driven decision-support tool that can help operators improve service quality and serve as a basis for future integration with operational and environmental indicators in sustainable last-mile delivery planning. Full article

With growing emphasis on green and low-carbon development and rising urban delivery demand, electric vehicles (EVs) have been increasingly adopted in logistics distribution systems. However, their limited driving range, relatively long charging durations, and the limited capacity of charging stations pose substantial challenges to real-world electric delivery operations. When multiple vehicles arrive at a station with a limited number of chargers, queueing delays may disrupt subsequent customer service and increase total operating costs. To address this issue, this study investigates an electric vehicle routing problem with capacitated charging stations and queueing delays. A mixed-integer linear programming model is formulated, and an enhanced adaptive large neighborhood search (ALNS) algorithm is developed to efficiently solve medium- and large-scale instances. In the proposed model, each vehicle visit to a charging station is represented as a charging event, while finite station capacity is enforced through charging-event assignment and temporal non-overlap constraints. Computational results show that the enhanced ALNS matches the proven optimal solution for the 10-customer instance. For the 15- and 20-customer instances, the best objective values obtained by the enhanced ALNS were 0.39% and 4.84% lower than the corresponding time-limited Gurobi incumbents, respectively. For the 30-, 50-, and 100-customer instances, the enhanced ALNS consistently generates feasible solutions within the prescribed computational budget, whereas Gurobi does not obtain a feasible incumbent within substantially longer time limits. Compared with the baseline ALNS, the enhanced version generally achieves lower mean objective values and more favorable convergence behavior. Sensitivity analyses further show that increasing the number of chargers and improving the charging rate can reduce queueing delays and total charging duration. The proposed approach provides practical decision support for reliable and sustainable urban electric freight operations. Full article

Urban last-mile delivery is a rapidly growing source of city-centre emissions, and decarbonizing it without eroding service quality has become imperative for climate goals. Operators are turning to multimodal systems that integrate road vehicles, off-peak metro freight, and electric drones—yet the optimal delivery channel varies dynamically with location and time. Current RL-based schedulers handle constraints via manually tuned penalty weights, lacking formal safety guarantees, and the feasibility of online carbon-cap enforcement under partial observability remains an open question. To address this, we model the problem as a Constrained Markov Decision Process (CMDP) and propose a demonstration-regularized Lagrangian deep RL algorithm. Our approach learns an online policy that is model-free at deployment—it controls emissions in expectation against a hard carbon budget, makes per-order decisions using only state observations, and operates without an emission model at test time (the demonstrator used at training time does access the emissions model, so “model-free” refers strictly to the deployment phase). Experiments on synthetic benchmarks and a Nanjing-inspired scenario—grounded in real metro topology and population-weighted demand—show that our policy achieves emissions within 1.3% of the offline optimum. It robustly tracks a ±17% carbon-budget band across a threefold daily volume range and a threefold city-scale range, with zero per-instance tuning. By contrast, a standard PPO with fixed penalty weights consistently degrades to single-mode selection. Our findings suggest that hard carbon budgets can be controlled in expectation online at modest cost—a step toward operator-facing low-carbon logistics whose average emissions honour a binding carbon budget, though external validation on operational data and a risk-sensitive formulation that upgrades this average control into per-day compliance are still required before deployment. Full article

This study investigates the key drivers and barriers influencing the adoption of electric vehicles (EVs) in urban freight logistics, using Lithuania as a case study. An integrated methodological framework combining Delphi, Fuzzy logic, DEMATEL, and System Dynamics is applied to identify critical factors and analyse their interdependencies. Four main drivers are identified: infrastructure, acquisition costs, technological development, and policy measures. Expert evaluations are transformed into fuzzy values to quantify factor importance, which are then incorporated into a dynamic simulation model to assess EV adoption and CO₂ emission trends. In addition to baseline scenarios, extreme scenario analysis is conducted to evaluate system sensitivity to economic, technological, and policy changes. The results reveal strong nonlinear relationships between factors and highlight the importance of their balanced development. The findings suggest that rapid EV adoption in urban logistics requires a coordinated approach integrating infrastructure expansion, financial incentives, technological progress, and policy support. The study provides practical insights for policymakers and logistics companies aiming to accelerate sustainable urban transport transitions. Full article

As passenger travel and freight transport demand continue to rise, transportation remains a major source of global carbon emissions. Predicting transportation carbon emissions helps support sustainable transportation planning and the development of scientifically sound emission reduction policies. However, there are currently no unified standards for accounting for transportation carbon emissions, and different approaches yield widely varying predictions, creating challenges for formulating carbon emission policies. In this study, a top–down approach based on China’s energy statistical data is adopted to calculate regional transportation carbon emissions (TCE). Twenty factors influencing carbon emissions are selected, and Spearman correlation analysis is used to examine the correlations among these factors. Lasso regression and the STIRPAT model are employed to quantitatively analyze the influence of each factor. Subsequently, an improved hybrid deep learning model, GA–CNN–LSTM–Attention, is proposed to predict carbon emissions under baseline, restriction, and control scenarios. Finally, an empirical analysis is grounded in a case study of Fujian Province, China. The quantitative analysis results show that, of all the factors, the urbanization rate has the greatest impact on transportation carbon emissions in the region, and the GA–CNN–LSTM–Attention prediction model achieves higher forecasting accuracy. Under the first two scenarios, carbon emissions in the region show a year-by-year increasing trend from 2022 to 2035, with no emissions peak observed. Under the controlled scenario, carbon emissions are projected to peak in 2030, reaching approximately 70.28 million tons of CO₂. The prediction results provide a scientific basis for sustainable transportation development and government policymaking on carbon reduction. Full article

Sustainable transport transformation increasingly depends on the configuration and performance of urban infrastructure systems. In coastal and port cities, decarbonizing transport is particularly complex due to spatial constraints, heritage protection requirements, and the coexistence of freight and passenger flows. In such environments, accessibility functions as a key indicator of transport infrastructure performance, reflecting how effectively transport systems enable low-carbon and multimodal mobility choices. Gdynia, a major Baltic port city in Poland, represents a context in which infrastructure limitations intersect with growing mobility demand. The concentration of port-related traffic, compact urban form, and limited opportunities for network expansion create structural conditions that may reinforce car dependency. This study examines infrastructure and accessibility challenges at the micro-scale of the Faculty of Navigation at Gdynia Maritime University, a centrally located campus with limited integration into public and active transport systems. Based on a survey of 342 respondents, including students and employees, the research analyzes modal split, travel time, and perceived barriers to sustainable mobility. The findings reveal infrastructure gaps in public transport connectivity, cycling network integration, and parking policy, collectively influencing transport behavior and constraining the shift toward low-carbon mobility. The study highlights the importance of infrastructure alignment, intermodal integration, and accessibility-based planning as prerequisites for smart and sustainable transport systems in coastal areas. Full article

Sustainable and resilient road infrastructure requires the early identification of pavement deterioration mechanisms that emerge under complex urban traffic conditions, particularly at signalized intersections where repeated stop–go operations, queue persistence, and lane-wise freight concentration generate highly nonuniform structural loading. However, most existing intelligent transportation studies emphasize crash prediction, traffic-state estimation, or mobility optimization, while the infrastructure-performance consequences of freight-dominant interrupted flow remain insufficiently addressed. To support proactive pavement management and resilient urban road operation, this study proposes a traffic simulation-driven deep learning framework for predicting lane-level pavement deterioration under freight loading and stop–go urban traffic conditions. A high-resolution PTV Vissim 2024 microscopic simulation environment was developed for a four-leg signalized urban intersection, and a structured multi-scenario design was used to generate progressively increasing operational stress regimes, ranging from baseline flow to freight-dominant oversaturated operation. The resulting lane-wise dataset integrates direct traffic variables with pavement-oriented descriptors, including the Lane Freight Loading Index (LFLI), Stop–Go Severity Index (SGSI), ESAL proxy, queue persistence, and Loading Asymmetry Index (LAI). To learn the complex relationship between traffic operation and infrastructure degradation, a new Freight-Aware Lane Interaction Transformer Network (FLIT-Net) is introduced. The proposed model combines feature embedding, lane-interaction self-attention, freight-aware gating, residual refinement, and multi-task regression to jointly predict rutting risk, fatigue-cracking risk, and the Pavement Deterioration Index (PDI). Experimental results show that FLIT-Net outperforms MLP, CNN, LSTM, Bi-LSTM, and generic Transformer baselines, achieving RMSE/MAE/$R^2$ values of 0.041/0.032/0.9687 for rutting risk, 0.044/0.034/0.9635 for fatigue-cracking risk, and 0.031/0.024/0.9824 for PDI. Sensitivity and scenario-wise analyses further confirm that deterioration increases monotonically with freight intensity, stop–go severity, and queue persistence, highlighting the importance of lane-resolved deterioration intelligence for sustainable maintenance prioritization. The proposed framework bridges traffic microsimulation, pavement-oriented feature engineering, and freight-aware deep learning, providing a decision-support basis for improving the performance, safety, and resilience of urban pavement infrastructure. Full article

Urban logistics supports retail, public services, healthcare, construction, and household consumption, but delivery decisions often fail to account for the social costs imposed on congested roads, curbside space, air quality, noise exposure, safety, and public-space use. This study addresses the decision-maker mismatch that arises when the vehicle operator physically generates an externality while receivers, shippers, platforms, building managers, or consumers control delivery timing, shipment fragmentation, service level, fleet choice, and receiving conditions. It develops a Pigouvian policy framework that integrates dynamic road-user charging, curbside pricing, emission-based instruments, off-hour delivery incentives, clean-vehicle support, consolidation incentives, and revenue recycling. The study combines a structured narrative synthesis, decision-maker mapping, an illustrative Korean urban logistics scenario, cost–benefit comparison, and deterministic sensitivity screening. Under the stated scenario assumptions, a carrier-only peak charge reduces monetized daily external costs by only 1.3%, whereas a combined road-and-curbside package reduces them by 12.5%, and an integrated Pigouvian package reduces them by 23.6%. Sensitivity results preserve this ranking. The paper contributes a transferable policy design architecture for internalizing urban logistics externalities while maintaining freight functionality and stakeholder acceptability. Full article