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Mobility-as-a-Service (MaaS) system is regarded as one of the emerging solutions to offer integrated, seamless, and flexible multi-modal mobility services as an alternative to privately owned mobility resources. MaaS is expected to change the way users will choose their modes of transport to reach their daily activities, and how service providers will generate profits, cooperate, and compete. To successfully deploy MaaS to reach the intended goals, it is critical to develop feasible and sustainable models that capture the diverse needs of customers as well as the diverse and often competing objectives of service providers. This paper aims to provide a general modelling framework and a critical and descriptive analysis of the relevant literature relating all main actors in the MaaS ecosystem, and identify and discuss all factors that are considered relevant, focusing on the actor’s decision-making processes and their correlations. This review shows the large variety and interaction of factors influencing MaaS adoption and their impact on forecasting MaaS appeal. It is also observed that current travel behaviour and multi-modal transport models are not fully capturing the diverse travel needs and choices of potential MaaS users. Recent advancements in agent-based simulation and discrete choice modelling offer potential solutions to address this gap, and future research should aim in that direction. Finally, the review analyses the interaction between MaaS actors, including customers, service providers, the government, and the MaaS Broker, highlighting the complexity of the modelling process comprising all actors of the MaaS ecosystem. Therefore, it is recommended to prioritise future research in exploring these areas.

Accessibility is a key dimension for sustainable transport network management and planning. However, conventional location-based accessibility measures typically rely on average travel times as the sole temporal metric, neglecting detailed travel time distributions. Consequently, these methods yield identical accessibility values for study zones with the same mean travel time but different travel time variations. To overcome this limitation, we developed a novel approach that explicitly integrates the probability density distributions of travel times, modelling the impact of travel time variability on accessibility. We applied the proposed method using GPS data collected from taxis in Harbin, China, and compared its outcomes with those from existing potential accessibility calculations. Across all 103 study zones in Harbin, the existing method underestimated the accessibility by 6–28%, with an average underestimation of 17% when benchmarked against the new method. These inaccuracies also impaired the identification of urban areas with the lowest accessibility levels, leading to the misclassification of 20% of problematic zones. The findings highlight the limitations of existing methods, which produce biassed accessibility estimations and misleading results. In contrast, the proposed travel time variability-integrated accessibility measure demonstrates greater sensitivity to actual traffic conditions, providing a more accurate and objective assessment of network performance.

This paper focuses on the effect of weather conditions on daily traffic intensities (the number of cars passing a specific segment of a road). The main objective is to examination whether or not weather conditions uniformly alter daily traffic intensities in Belgium, or in other words whether or not road usage on a particular location determines the size of the impacts of various weather conditions. This general examination is a contribution that allows policymakers to assess the appropriateness of countrywide versus local traffic management strategies. In addition, a secondary goal of this paper is to validate findings in international literature within a Belgian context. To achieve these goals, the paper analyzes the effects of weather conditions on both upstream (toward a specific location) and downstream (away from a specific location) traffic intensities at three traffic count locations typified by a different road usage. Perhaps the most interesting results of this study for policymakers are the heterogeneity of the weather effects between different traffic count locations, and the homogeneity of the weather effects on upstream and downstream traffic at specific locations. The results also indicate that snowfall, rainfall, and wind speed diminish traffic intensity, and high temperatures increase traffic intensity. Further generalizations of the findings might be possible by studying weather impacts on local roads and by shifting the focus of research toward travel behavior.

Land use change models enable the exploration of the drivers and consequences of land use dynamics. A broad array of modeling approaches are available and each type has certain advantages and disadvantages depending on the objective of the research. This paper presents an approach combining cellular automata (CA) model and support vector machines (SVMs) for modeling urban land use change in Wallonia (Belgium) between 2000 and 2010. The main objective of this study is to compare the accuracy of allocating new land use transitions based on CA-SVMs approach with conventional coupled logistic regression method (logit) and CA (CA-logit). Both approaches are used to calibrate the CA transition rules. Various geophysical and proximity factors are considered as urban expansion driving forces. Relative operating characteristic and a fuzzy map comparison are employed to evaluate the performance of the model. The evaluation processes highlight that the allocation ability of CA-SVMs slightly outperforms CA-logit approach. The result also reveals that the major urban expansion determinant is urban road infrastructure.

This study investigates the meteorological variation in revealed preference travel data. The main objective of this study is to investigate the impact of weather conditions on daily activity participation (trip motives) and daily modal choices in the Netherlands. To this end, data from the Dutch National Travel Household Survey of 2008 were matched to hourly weather data provided by the Royal Dutch Meteorological Institute and were complemented with thermal indices to indicate the level of thermal comfort and additional variables to indicate the seasonality of the weather conditions. Two multinomial logit–generalised estimation equations (MNL-GEE) models were constructed, one to assess the impact of weather conditions on trip motives and one to assess the effect of weather conditions on modal choice. The modelling results indicate that, depending on the travel attribute of concern, other factors might play a role. Nonetheless, the thermal component, as well as the aesthetical component and the physical component of weather play a significant role. Moreover, the parameter estimates indicate significant differences in the impact of weather conditions when different time scales are considered (e.g. daily versus hourly based). The fact that snow does not play any role at all was unexpected. This finding can be explained by the relatively low occurrence of this weather type in the study area. It is important to consider the effects of weather in travel demand modelling frameworks because this will help to achieve higher accuracy and more realistic traffic forecasts. These will in turn allow policy makers to make better long-term and short-term decisions to achieve various political goals, such as progress towards a sustainable transportation system. Further research in this respect should emphasise the role of weather conditions and activity-scheduling attributes.

The issue of energy retrofitting of existing building stock occupies an increasingly prominent place in energy transition strategies in Europe. Adopting models representing the building stock and accounting for occupancy influence on final housing energy use must be developed to advise new policies. In this respect, this study aims to characterize the Walloon residential building stock and to analyze the existing correlations between the stock’s technical data and its occupants’ socioeconomic data. This study uses existing databases on buildings and inhabitants in Wallonia. Several statistical analyses make it possible to highlight the preponderant criteria and existing correlations between these different criteria. This study affirms the importance of accounting for certain socioeconomic categories, such as low-income groups, in a global strategic reflection on energy renovation. Multiple linear regression shows us that each percent increase in the category of households that declare between 10,000–20.000 EUR of income per year corresponds to an increase of 7.22 kWh/m2·y in the average energy efficiency of the built stock. The results highlight the importance of focusing on renovation strategies for particular types of buildings, such as semi-detached houses, which combine unfavorable technical and socioeconomic factors. Thus, the results confirm the interest of a mixed model approach to adapt to effective renovation policy strategies.

An in-depth understanding of the main factors behind built-up development is a key prerequisite for designing policies dedicated to a more efficient land use. Infill development policies are essential to curb sprawl and allow a progressive recycling of low-density areas inherited from the past. This paper examines the controlling factors of built-up expansion and densification processes in Wallonia (Belgium). Unlike the usual urban/built-up expansion studies, our approach considers various levels of built-up densities to distinguish between different types of developments, ranging from low-density extensions (or sprawl) to high-density infill development. Belgian cadastral data for 1990, 2000 and 2010 were used to generate four classes of built-up areas, namely, non-, low-, medium- and high-density areas. A number of socioeconomic, geographic and political factors related to built-up development were operationalised following the literature. We then used a multinomial logistic regression model to analyse the effects of these factors on the transitions between different densities in the two decades between 1990 and 2010. The findings indicate that all the controlling factors show distinctive variations based on density. More specifically, the centrality of zoning policies in explaining expansion processes is highlighted. This is especially the case for high-density expansions. In contrast, physical and neighbourhood factors play a larger role in infill development, especially for dense infill development. 深入了解建设发展背后的主要因素是设计更高效土地利用政策的关键先决条件。填补式开发政策对于遏制蔓延和逐步更新历史遗存的低密度地区至关重要。本文考察了瓦隆（比利时）建设膨胀和致密化过程的控制因素。与通常的城市/扩建研宄不同，我们的方法考虑不同层次的建设密度，以区分从低密度扩张（或蔓延）到高密度填充开发的不同开发类型。文章利用1990年、 2000 年和 2010年的比利时地籍数据生成了四类建设区，即无密度、低密度、中密度和高密度地区。我们根据研宄文献，对与建设发展有关的一些社会经济、地理和政治因素做了操作。然 后，我们使用多项逻辑回归模型来分析这些因素对1990年至2010年这二十年间不同密度之间转变的影响。研宄结果表明，所有控制因素都显示出基于密度的独特变化。更具体地说，分区政策在解释扩张过程中的中心地位是突出的。高密度扩展尤其如此。与之形成对比的，物理因素和邻里因素在填充式开发中起到了较大的作用，特别是对于密集填充式的开发。

The current literature on public perceptions of autonomous vehicles focuses on potential users and the target market. However, autonomous vehicles need to operate in a mixed traffic condition, and it is essential to consider the perceptions of road users, especially vulnerable road users. This paper builds explicitly on the limitations of previous studies that did not include a wide range of road users, especially vulnerable road users who often receive less priority. Therefore, this paper considers the perceptions of vulnerable road users towards sharing roads with autonomous vehicles. The data were collected from 795 people. Extreme gradient boosting (XGBoost) and random forests are used to select the most influential independent variables. Then, a decision tree-based model is used to explore the effects of the selected most effective variables on the respondents who approve the use of public streets as a proving ground for autonomous vehicles. The results show that the effect of autonomous vehicles on traffic injuries and fatalities, being safe to share the road with autonomous vehicles, the Elaine Herzberg accident and its outcome, and maximum speed when operating in autonomous are the most influential variables. The results can be used by authorities, companies, policymakers, planners, and other stakeholders.

Walkability has become a research topic of great concern for preserving public health, especially in the era of the COVID-19 outbreak. Today more than ever, urban and transport policies, constrained by social distancing measures and travel restrictions, must be conceptualized and implemented with a particular emphasis on sustainable walkability. Most of the walkability models apply observation and subjective methods to measure walkability, whereas few studies address walkability based on sense perception. To fill this gap, we aim at investigating the perceived neighbourhood walkability (PNW) based on sense perception in a neighbourhood of Brussels. We designed a survey that integrates 22 items grouped into 5 dimensions (cleanness, visual aesthetics, landscape and nature, feeling of pressure, feeling of safety), as well as the socio-demographic attributes of the participants. Using various statistical methods, we show that socio-demographics have almost no effects on perceived neighbourhood walkability. Nonetheless, we found significant differences between groups of different educational backgrounds. Furthermore, using a binomial regression model, we found strong associations between PNW and at least one item from each grouping dimension. Finally, we show that based on a deep neural network for classification, the items have good predictive capabilities (78% of classification accuracy). These findings can help integrate sense perception into objective measurement methods of walkable environments. Additionally, policy recommendations should be targeted based on differences of perception across socio-demographic groups.

Between the ages of 6 and 18, children spend between 30 and 42 h a week at school, mostly indoors, where indoor environmental quality is usually deficient and does not favor learning. The difficulty of delivering indoor air quality (IAQ) in learning facilities is related to high occupancy rates and low interaction levels with windows. In non-industrialized countries, as in the cases presented, most classrooms have no mechanical ventilation, due to energy poverty and lack of normative requirements. This fact heavily impacts the indoor air quality and students’ learning outcomes. The aim of the paper is to identify the factors that determine acceptable CO2 concentrations. Therefore, it studies air quality in free-running and naturally ventilated primary schools in Chile, aiming to identify the impact of contextual, occupant, and building design factors, using CO2 concentration as a proxy for IAQ. The monitoring of CO2, temperature, and humidity revealed that indoor air CO2 concentration is above 1400 ppm most of the time, with peaks of 5000 ppm during the day, especially in winter. The statistical analysis indicates that CO2 is dependent on climate, seasonality, and indoor temperature, while it is independent of outside temperature in heated classrooms. The odds of having acceptable concentrations of CO2 are bigger when indoor temperatures are high, and there is a need to ventilate for cooling.