Drivers and barriers to energy-efficient technologies (EETs) in EU residential buildings

To achieve carbon targets, the European Union (EU) aims to promote nearly zero-energy buildings (nZEB). To enable the necessary transition, technical solutions need to converge with socio-economic factors, such values and awareness of stakeholders involved in the decision-making process. In this light, the aim of this paper is to characterise perceived drivers and barriers to nine energy-efficient technologies (EET), according to key decision-makers\u27 and persuaders of the technology selection in the EU residential building context. Results are collected across eight EU countries, i.e. Belgium (BE), Germany (DE), Spain (ES), France (FR), Italy (IT), Netherlands (NL), Poland (PL), and United Kingdom (UK). The stakeholders’ selected are architects, construction companies, engineers, installers and demand-side actors. Data from a multi-country survey is analysed to calculate the share of 15 drivers and 21 barriers (aggregated to 5 groups), being selected for each EET and country. The 5 groups considered to analyse drivers and barriers are environmental, technical, economic, social, legal. The perceived barriers and drivers were further studied for their association across the countries using the Pearson\u27s Chi2 and a Cramer\u27s V tests. The results demonstrate that across all EETs and countries, the technical and economic driver groups are perceived to have the highest potential to increase the implementation rate of EET. In terms of barriers, economic aspects are seen as the foremost reason that EET are not scaling faster. In both drivers and barriers legal aspects are the least often selected. In overall the barrier groups show significant variation across countries compared to driver groups. These findings provide an evidence-basis to better understand arguments in favour and against specific EETs and, in this way, support policy makers and other interested parties to increase the market share of the selected solutions

A freight origin-destination synthesis model with mode choice

This paper develops a novel procedure to conduct a Freight Origin-Destination Synthesis (FODS) that jointly estimates the trip distribution, mode choice, and the empty trips by truck and rail that provide the best match to the observed freight traffic counts. Four models are integrated: (1) a gravity model for trip distribution, (2) a binary logit model for mode choice, (3) a Noortman and Van Es’ model for truck, and (4) a Noortman and Van Es’ model for rail empty trips. The estimation process entails an iterative minimization of a nonconvex objective function, the summation of squared errors of the estimated truck and rail traffic counts with respect to the five model parameters. Of the two methods tested to address the nonconvexity, an interior point method with a set of random starting points (Multi-Start algorithm) outperformed the Ordinary Least Squared (OLS) inference technique. The potential of this methodology is examined using a hypothetical example of developing a nationwide freight demand model for Bangladesh. This research improves the existing FODS techniques that use readily available secondary data such as traffic counts and link costs, allowing transportation planners to evaluate policy outcomes without needing expensive freight data collection. This paper presents the results, model validation, limitations, and future scope for improvements

Enabling Factors and Durations Data Analytics for Dynamic Freight Parking Limits

Freight parking operations occur amid conflicting conditions of public space scarcity, competition with other users, and the inefficient management of loading zones (LZ) at cities’ curbside. The dynamic nature of freight operations, and the static LZ provision and regulation, accentuate these conflicting conditions at specific peak times. This generates supply–demand mismatches of parking infrastructure. These mismatches have motivated the development of Smart LZ that bring together technology, parking infrastructure, and data analytics to allocate space and define dynamic duration limits based on users’ needs. Although the dynamic duration limits unlock the possibility of a responsive LZ management, there is a narrow understanding of factors and analytical tools that support their definition. Therefore, the aim of this paper is twofold. Firstly, to identify factors for enabling dynamic parking durations policies. Secondly, to assess data analytics tools that estimate freight parking durations and LZ occupation levels based on operational and locational features. Semi-structured interviews and focus group analyses showed that public space use assessment, parking demand estimation, enforcement capabilities, and data sharing strategies are the most relevant factors when defining dynamic parking limits. This paper used quantitative models to assess different analytical tools that study LZ occupation and parking durations using tracked freight parking data from the City of Vic (Spain). CatBoost outperformed other machine learning (ML) algorithms and queuing models in estimating LZ occupation and parking durations. This paper contributes to the freight parking field by understanding how data analytics support dynamic parking limits definition, enabling responsive curbside management

Energy-efficient retrofit measures (EERM) in residential buildings: An application of discrete choice modelling

Cross-country evidence on the adoption of energy-efficient retrofit measures (EERMs) in residential buildings is critical to supporting the development of national and pan-European policies aimed at fostering the energy performance upgrade of the building stock. In this light, the aim of this paper is to advance in the understanding of the probability of certain EERMs taking place in eight EU countries, according to a set of parameters, such as building typology, project types, and motivation behind the project. Using these parameters collected via a multi-country online survey, a set of discrete-choice (conditional logit) models are estimated on the probability of selecting a choice of any combination of 33 EERMs across the sampled countries. Results show that actions related to the building envelope are the most often-addressed across countries and single building elements or technology measures have a higher probability of being implemented. The modelling framework developed in this study contributes to the scientific community in three ways: (1) establishing an empirical relationship among EERMs and project (i.e., retrofit and deep retrofit), (2) identifying commonalities and differences across the selected countries, and (3) quantifying the probabilities and market shares of various EERMs

Joint modeling of arrivals and parking durations for freight loading zones: Potential applications to improving urban logistics

This paper analyzes truck parking patterns in urban freight loading zones by jointly modeling the vehicle arrival rates and the parking durations. Three models were explored: 1) Count data (Negative Binomial) for vehicle arrivals, 2) Survival (Weibull) model for parking duration and 3) A joint model for arrivals and duration. The count data model estimates the parking demand i.e., the rate of truck arrival, while the survival model estimates the probability that a truck is parked for one more minute. The joint model is compared with separate models for predictability and performance. The dataset used in this research is obtained using a mobile phone parking application, at eight loading zones in the city Vic, Spain over an 18-month period from July 2018 to December 2019, comprised of vehicle parking durations, date, time of arrival and departure, professional activity, and vehicle type (weight). The parking activity data are complemented with built in environment variables of the loading zones, such as the number of establishments in a certain radius, the average walking distance to establishments, the presence of pedestrian pavement, the number of traffic lanes, among others. The joint model outperforms the models estimating the arrival rates and durations separately in goodness of fit and predictability. The model results showed that truck arrival rates vary significantly across days of the week, months, and arrival times. The parking durations are highly dependent on professional activity, vehicle type, and size. Tuesdays and Wednesdays have higher arrival rates compared to other days of a week (except Sundays). Among activities, the transport and parcels require longer parking durations. Among the vehicle types, trucks with gross weight larger than 3.5 tons park longer. This paper concludes by explaining the potential of these modeling approaches in improving urban freight operations, evaluation of various policy implications, limitations, and future research

Assessing the eco-efficiency benefits of empty container repositioning strategies via dry ports

Trade imbalances and global disturbances generate mismatches in the supply and demand of empty containers (ECs) that elevate the need for empty container repositioning (ECR). This research investigated dry ports as a potential means to minimize EC movements, and thus reduce costs and emissions. We assessed the environmental and economic effects of two ECR strategies via dry ports—street turns and extended free temporary storage—considering different scenarios of collaboration between shipping lines with different levels of container substitution. A multiparadigm simulation combined agent-based and discrete-event modelling to represent flows and estimate kilometers travelled, CO2 emissions, and costs resulting from combinations of ECR strategies and scenarios. Full ownership container substitution combined with extended free temporary storage at the dry port (FTDP) most improved ECR metrics, despite implementation challenges. Our results may be instrumental in increasing shipping lines’ collaboration while reducing environmental impacts in up to 32 % of the inland ECR emissions

Efficiency effects of information on operational disruption management in port hinterland freight transport: simulation of a Swedish dry port case

Management of operational disruptions with support of information is essential to facilitate the shift from road to rail and to ensure efficient hinterland intermodal transport chains. Therefore, the purpose of this paper is to investigate the operational efficiency effects of information on operational disruption management in hinterland transport with a dry port to facilitate efficient intermodal hinterland transport. For that purpose, a simulation model with five scenarios was developed and applied using empirical data from a real-world case of a hinterland transport chain with a dry port. The results show that the resource utilisation of the trucks that deliver containers from the dry port to the receivers can be increased using the information that supports management of the disruption. Nevertheless, in attempts to increase resource utilisation when managing the disruption, issues arose from efficiency measures that are important for other actors, e.g. the receivers

Using Machine Learning to Predict Freight Vehicles\u27 Demand for Loading Zones in Urban Environments

This paper studies demand for public loading zones in urban environments and seeks to develop a machine learning algorithm to predict their demand. Understanding and predicting demand for public loading zones can: (i) support better management of the loading zones and (ii) provide better pre-advice so that transport operators can plan their routes in an optimal way. The methods used are linear regression analysis and neural networks. Six months of parking data from the city of Vic in Spain are used to calibrate and test the models, where the parking data is transformed into a time-series format with forecasting targets. For each loading zone, a different model is calibrated to test which model has the best performance for the loading zone\u27s particular demand pattern. To evaluate each model\u27s performance, both root mean square error and mean absolute error are computed. The results show that, for different loading zone demand patterns, different models are better suited. As the prediction horizon increases, predicting further into the future, the neural network approaches start to give better predictions than linear models

Service trip attraction in commercial establishments

Commercial traffic in urban areas has not received the level of attention it deserves. Notwithstanding recent research on freight trip generation, other components of commercial traffic, such as commercial service traffic, have been largely overlooked. This is ironic, as the service sector represents a major and growing portion of urban and metropolitan economies. The research reported in this paper intends to help fill an important research gap through analyses of unique survey data collected by the authors. To this effect, the research comprehensively characterizes service visits to commercial establishments?in terms of frequency, purpose, duration, time of day, and other characteristics?by industry sector for two metropolitan areas. In addition, the authors estimated econometric models that express the number of service trips to commercial establishments as a function of the economic characteristics of the establishment and assessed the geographic transferability of the models obtained. To gain insight into the overall magnitude of service-related traffic, the models were applied to publicly available data to estimate the service activity in American cities of various sizes. The resulting service traffic are then used to estimate of parking requirements of service and freight vehicles for the most congested ZIP codes at these cities. The paper ends with a discussion of chief findings and policy implications

Freight mode choice: Results from a nationwide qualitative and quantitative research effort

The research reported in this paper focused on studying various aspects of freight mode choice in the continental United States (US) including the influencing factors, the development of econometric models to assess the impacts of public-sector policies and changes in market conditions. To gain insight into this complex subject, the team used qualitative and quantitative research techniques. The qualitative effort involved In-Depth Interviews (IDIs) with a highly selective group of leading shippers, carriers, and receivers. The IDIs provided insight into the key factors that influence mode choice, and the barriers that limit mode shifts. The quantitative effort estimated econometric models that express freight mode choice as a function of key independent variables. A unique aspect of this research is that the models were estimated using high-quality confidential data under the custody of the United States’ Census Bureau, the Internal Revenue Service, and the Surface Transportation Board, including: the Commodity Flow Survey (CFS), the largest shipper survey in the world; the Longitudinal Business Database (LBD), a comprehensive registry of commercial establishments in the US; and the Waybill Sample, a 5% sample; together with custom-made datasets of modal characteristics prepared by the authors. Using these data, the team estimated discrete-continuous freight mode choice models representing the choice of rail or truck for 42 different commodity types, and different combinations of independent variables and weighting schemes. The paper concludes with a discussion of the policy implications of the research conducted