Electric vehicles (EVs) are often promoted as a solution to the impacts of transport on the climate since their GHG emissions are generally less than those of Internal Combustion Engine Vehicles (ICEVs). Considering only tail-pipe emissions, EVs are zero-emission vehicles and are being promoted as a sustainable mode. Hence, many likely believe that having no tail-pipe emissions makes EVs a robust solution to climate change. However, in the current context, EVs do not have significantly lower life-cycle GHG emissions than ICEVs. Further, EVs do not address many other externalities of vehicle use, such as health impacts or congestion. As the running costs of EVs are less than ICEVs, people would likely drive them more, which could exacerbate various externalities. This research examines the opinions of Canadians with driver's licenses concerning such questions. It further examines how such beliefs might influence decisions to purchase an EV. That analysis details whether it would replace an ICEV or be an additional vehicle and what influences those outcomes. Hence, a survey was conducted, and an interpretable machine learning method was developed. The results suggest that 18.7% (95% confidence intervals: 17.1%-20.5%) of Canadians anticipate driving more due to the lower cost per kilometer of driving an EV. Moreover, the potential EV purchasers are more likely to drive more, which could exacerbate various externalities. Those worried about climate change are also more likely to drive more if they own EVs. The results suggest problems related to a rebound effect, where behavioral reactions could create other problems.

Climate change is a global challenge, making this a crucial time for altering human behaviors to mitigate its effects. This study investigates the difficulty or ease of different climate change-related behaviors, particularly those associated with transportation. To this end, the Rasch model is employed. This paper also intends to examine the link between those behaviors and a robust measure to evaluate individuals’ environmental behaviors and attitudes, called the Climate Change Stage of Change (CC-SoC). In this regard, a machine learning method ranks various climate change-related behaviors according to their influence on CC-SoC. The findings indicate that transport-based actions are generally among the most challenging to change, with living without a vehicle being the most difficult. Avoiding long-haul flights, using an electric vehicle, and riding an electric-assist bicycle were within the top five determinants of CC-SoC, indicating the strong influence of transport-related behaviors on climate change. The findings of this study are critical for informing transport policy, since they help identify which behavioral shifts are most impactful yet most resistant to change, allowing for more targeted and effective interventions.

Understanding people's environmental behaviors is essential to combating climate change. However, certain behaviors are harder for some people while being easier for others. As such, it is essential to identify the difficulty level of various behaviors for better targeting policies and interventions. Therefore, the objective of this study is to first determine the difficulty of environmental behaviors with consideration given to their GHG reduction and, second, to identify the difficulty of such behaviors across different segments of the population, focusing on the difficulty of transportation-related behaviors. A large-scale survey was conducted, and the Rasch model was used for modeling. The results suggested that low-impact and medium-impact behaviors were much easier for Canadians to adopt than high-impact behaviors. Transportation-related behaviors were generally among the hardest actions for the population. Among these, using a plug-in hybrid car was the simplest, while living vehicle-free was the most difficult.

One of the factors significantly contributing to EV preferences is how greenhouse gas (GHG) information is presented. This study aimed to identify the best GHG information presentation to communicate with different populations. Therefore, seven new framings for GHG information presentation were tested based on theories such as psychological distancing. The performance of these framings was compared with the GHG information presentation applied to current vehicle labels in Canada. A survey of over 2000 participants was administered. The optimal label for communicating with different populations was identified, and the influence of different framings on EV preference probability was calculated. The results confirmed the importance of GHG information presentation on EV preference/choice. Further, it was postulated that different framings should be applied to communicate with various populations. Some of the framings developed in this study could increase the EV preference probability by over 20% (e.g., disaster-based framing for residents of Alberta).

The forest transportation sector is a significant source of greenhouse gas emissions. Industrial professionals aim to shift towards more environmentally friendly practices to help reduce emissions. Electrification is relatively new to forest transportation, as there are limited studies describing its influence because of limited practical use and a lack of relevant data on energy consumption and the behavior of electric trucks. This study investigates various opportunities and barriers to the adoption of battery electric trucks in forestry to support the emission reduction goals of Canada. This paper reviews the scientific literature relevant to studies in battery electric trucks in three planning horizons: strategic, tactical, and operational planning. It looks at the recent developments of heavy-duty electric trucks in charging infrastructure, life cycle analysis, total cost of ownership, energy consumption, emerging technology, and specific routing problems. This paper also discusses industrial initiatives in forest freight electrification. The analysis results highlight the different industrial applications in forestry where electrification brought about a watershed. The forest transportation sector has the potential to become carbon-neutral by investing in battery electric trucks, but achieving net-zero emissions might not be realistic without changes in policies and incentives.

Transportation is one of the most contributing economic sectors to carbon emissions. Increasing the knowledge of individuals to understand the negative effects of climate change can encourage them to make more eco-friendly decisions. Goal framing theory enhances the impact of climate change information on vehicle choice and different framings to present the CO2 emissions have different levels of effects on the willingness to pay (WTP) for the emission reduction. However, there might be some underlying psychological factors that induce these variations. This study introduces the Moral Foundations Values as moderating factors to explain such differences. The study demonstrates that individual moral values affect how people respond to these framings by analyzing data from discrete choice experiments with Canadian drivers. Specifically, the authority shows the strongest effects with the highest WTP under the Newspaper-fire framing. While it has the biggest negative effects on the WTP under Emojis framing. This suggests that using appropriate framing can considerably change the WTP for CO2 emissions considering the moral foundation values of different populations.

The urban multimodal transportation network is an essential urban infrastructure for daily mobility, while it is vulnerable to severe disturbances. Existing research often evaluates multimodal network resilience and criticality from either a structural or operational perspective, overlooking its multidimensional characterization. Most studies incorporating dynamic demand are conducted at daily or hourly intervals, neglecting finer temporal granularity that better captures network resilience and criticality. To address these gaps, this study proposes a comprehensive resilience evaluation method for multimodal transportation networks by integrating network structure and function. Node criticality is identified using a novel demand growth rate indicator. Various disturbance scenarios, including random and deliberate disturbances, are constructed to simulate sudden events, considering the impacts of the disturbance scale and intensity of nodes or edges. Moreover, an affected demand redistribution model is developed by combining graph convolutional network (GCN) and the Logit model, considering travel time, distance, transfer numbers, and path complexity. The proposed methods are applied to the multimodal transportation network in Tianjin, China, using transit smart card transaction data. Results reveal multimodal networks exhibit better resistance from a structural perspective, while the subway network achieves higher efficiency when the disturbance scale is less than 0.2. A threshold effect emerges between disturbance scale and residual passenger capacity. Node disturbances cause an average of 21% higher performance losses than edge disturbances. This method quantifies resilience and identifies the critical nodes considering minute-level dynamic travel demand, dynamic demand between nodes, and travel behaviors. These insights support decision-makers in generating more effective response strategies.

Mobility-as-a-Service (MaaS) has gained increasing worldwide popularity in the transport industry. However, it remains unclear how MaaS might diffuse over time and across space at the micro-scale. In response, we developed a spatial agent-based model to explore MaaS adoption and subscription (SABM-MaaS) in Beijing, China, which was based on the empirical findings derived from questionnaire survey data collected in Beijing in January 2020. In particular, we designed four MaaS plans, including pay-as-you-go, monthly, season (spanning three months), and yearly subscription plans, to observe which plan would be adopted by users. The results of the reference scenario revealed that the monthly plan (with a 20% discount) was the most prevalent, with almost one-fifth of agents adopting it, and it was followed by pay-as-you-go, season, and yearly plan. Moreover, we also set up several "what-if" scenarios to explore MaaS packages with different discount offerings, offering only one type of MaaS plan, and varying the intensity of MaaS advertisement. The findings also discovered that users still preferred the monthly subscription and pay-as-you-go options over long-term plans. Furthermore, while a strong intensity of MaaS advertisement had a substantial positive impact during the early stages of MaaS release, it did not sufficiently accelerate overall MaaS adoption and diffusion in the long run. The research outcomes provide valuable insights for the MaaS companies, multimodal transport operators, and demand-responsive transport planners in making better decisions regarding MaaS promotion, price and discount strategies, and shared mobility service planning.

The integration of theory-driven discrete choice modeling (DCM) with neural networks has demonstrated promising advances in choice behavior analysis, yet existing hybrid approaches face persistent challenges in parameter stability, overfitting, and computational efficiency. This study presents ResLogit Plus, a novel framework that enhances the synergy between residual neural networks and discrete choice models through genetic algorithm (GA) optimization. By leveraging GA for parameter initialization and hyperparameter tuning, our approach significantly improves model stability and computational performance while maintaining interpretability. We introduce a comprehensive validation framework incorporating regularization techniques and bootstrap-based validation to ensure robust parameter estimation and mitigate overfitting risks. Empirical validation is conducted using three datasets conducted in different countries: the Swiss Metro dataset (Switzerland), the Carpooling dataset (Switzerland and Germany), and the Greenhouse Gas Emissions dataset (Canada). The results reveal that ResLogit Plus achieves superior predictive accuracy compared to both the original ResLogit and traditional Multinomial Logit (MNL) models, while demonstrating enhanced parameter stability and reduced computational overhead. The framework effectively addresses key methodological challenges, including correlated alternatives and utility estimation noise, thereby advancing the field of discrete choice analysis through a balanced integration of predictive power and theoretical rigor.

This study examines the energy consumption and regenerative braking efficiency of battery electric buses (BEBs) using real-world data from Montreal's public transit network. Results show significant seasonal variations, with winter having the highest energy consumption due to heating demands and adverse road conditions, while summer has the lowest due to reduced auxiliary energy use and smoother traffic flow. Regenerative braking is most effective at mid-speed ranges (30-50 km/h), with peak efficiency in warmer months and a decline in winter, emphasizing environmental influences. Auxiliary heating and cooling demands significantly impact energy efficiency, especially in extreme climates. Cost analysis confirms BEBs' lower operating costs compared to diesel and hybrid buses. Optimizing BEB operations requires improvements in route planning, fleet scheduling, and charging strategies. By addressing previous data limitations, this study provides insights to enhance BEB efficiency and support their broader adoption in sustainable public transit systems.

Battery electric buses (BEBs) are increasingly deployed as a low-emission solution in public transit systems, yet their energy performance under cold climate conditions remains largely understudied. Seasonal changes in temperature and road conditions introduce significant variability in energy consumption, while empirical data from cold regions are limited. This study addresses this gap by analyzing over 66,000 real-world trip records from 29 electric buses operating in Montreal between June 2022 and October 2023. Key variables such as motion time, average speed, auxiliary heating demand, and regenerative braking efficiency were extracted and examined across seasons. Four models were tested for trip-level energy prediction, including multiple linear regression, random forest, XGBoost, and multilayer perceptron. The XGBoost model achieved the best performance (R squared equals 0.96, RMSE equals 1.33 kW h). Results indicate that energy consumption increases by up to 26 % in winter, driven by heating loads and adverse driving conditions, while regenerative braking efficiency declines from 53.4 % in summer to 32.2 % in winter. Speed also plays a critical role, with optimal energy recovery observed at 30-40 km/h. Despite seasonal variations in performance, BEBs maintain strong economic advantages over diesel alternatives. Findings underscore the need for adaptive operational strategies, such as temperatureaware scheduling, route optimization, and integrated charging planning, to optimize BEB deployment in cold regions. This research offers practical insights for transit agencies aiming to expand electrified fleets under variable climate conditions.

Traffic safety is undergoing a profound transformation, driven by advances in data science, sensing technologies, and computational modeling. Proactive approaches are enabling the early identification of potential hazards, real-time decision-making, and the development of smarter, safer transportation systems. This Special Issue summarizes recent progress in traffic safety assessment, highlighting the application of emerging tools such as machine learning, explainable artificial intelligence, and computer vision. These innovations are used to predict crash risks, evaluate surrogate safety measures, and automate the analysis of behavioral data, contributing to more inclusive and adaptive safety frameworks, particularly for vulnerable road users such as pedestrians and cyclists. The research also addresses key challenges, including data integration across diverse sources, aligning safety metrics with human perception, and ensuring the scalability of models in complex environments. By advancing both technical methodologies and human-centered evaluation, these developments signal a shift toward more intelligent, transparent, and equitable approaches to traffic safety assessment and policy-making.

Transportation is a major source of climate change emissions. Providing people with better information on those emissions is one means of helping individuals make climate-friendly choices. However, not everyone is influenced by the same type of information. Previous research has demonstrated that Goal Framing Theory could help improve the influence of climate change emissions information and that different framings have different levels of influence depending on a number of socio-demographic and attitudinal characteristics. However, apart from climate change motivation, what other underlying psychological factors might help us understand why the framings vary in their influence between individuals? Moral Foundation Theory (MFT) identifies key values that influence people's moral decisions, providing a useful framework for understanding diverse responses to information. The objective of this study is to understand whether MFT can help explain different responses by individuals and identify which framings are associated with stronger responses for different moral foundations. This study investigates the moderating effects of moral foundations on individuals' responsiveness to different emission information framings. Utilizing data from discrete choice experiments involving 2015 Canadian drivers, we examine how different moral foundations impact the willingness-to-pay (WTP) for reducing emissions. The results reveal that the impact of emissions information framing varies significantly according to individuals' moral foundations. Specifically, moral values associated with Authority, Fairness, and Purity play negative moderating roles on WTP for CO2 emissions under different framings, whereas Ingroup and Harm foundations have positive moderating effects on WTP with the framings tested. Additionally, innovative communication tools like new emojis demonstrated strong positive effects on WTP, especially among those with strong Ingroup, Fairness, and Purity values. Conversely, individuals with a strong Authority value showed the lowest WTP when presented with pressure gauge visuals. Using appropriate framing based on Moral Foundation Theory can considerably change the willingness-to-pay for climate change

Understanding the factors that will influence people's preferences for Electric Vehicles (EVs) over Internal Combustion Engine Vehicles (ICEVs) is crucial. A discrete choice experiment was designed and administered as an online survey resulting in 1077 completed questionnaires. This study examined the influence of over 83 variables on preferences for EVs. As well, previous studies have used tailpipe emissions only to present GHG information, but in this study lifecycle GHG emissions of vehicles are presented. Five ensemble learning techniques and two interpretation techniques were employed to investigate individual decisions regarding selecting between EVs and ICEVs. The results demonstrate that when lifecycle emissions are presented, financial impacts are the principal influences on predicting preference for an EV over ICEV. Following the financial impacts are existing preferences for EVs and attitudes related to climate change. How the emissions are presented was the 12th and 9th most influential factor for BEVs and PHEVs respectively.

To promote electric vehicles, it is vital to know what impacts the preferences for electric vehicles over conventional fuel-based cars. To address this, a discrete choice experiment is developed and integrated into a survey. An online survey was conducted in Canada with 2062 valid responses. Different labels are designed for the survey to determine the most effective GHG information framing to increase the influence of such information on decisions. In this study, the influence of lifecycle emissions is considered. Three ensemble learning techniques are applied and they are compared based on prediction accuracy, and the most accurate technique is applied to determine the relative influence of variables on the intention to buy electric vehicles. Further, the interaction of variables is investigated using xgbfir. Subsequently, Accumulated Local Effect (ALE) is employed to examine the influence direction of top variables on the electric vehicle purchase likelihood. The results suggest that environmental attitudes and purchase price are the most influential parameters on the intention to buy electric vehicles. Moreover, those who are extremely worried about climate change, do not own a car, and self-identified as being at the top of the climate change stage of change are more likely to buy electric vehicles.

To address the problem of climate change emissions from the transport sector, many countries are promoting electric vehicles (EVs). To support such efforts, it is essential to know what influences the choice of an EV over a traditional internal combustion engine vehicle (ICEV). To study this, a discrete choice experiment was developed, and 2,015 valid responses were gathered from Canadian adults with a driver's license. In place of a more traditional analysis, a machine learning approach, XGBoost, was applied. However, two key issues were addressed with respect to its application. First, a practical question related to how best to split the training and testing data was examined. A new technique based on the Coyote optimization algorithm (COA) is developed that automatically determines the split that leads to the greatest prediction accuracy. The policy-relevant results of the analysis found that an individual's Climate Change-Stage of Change (CC-SoC) and the price ratio of EVs to ICEVs are the most important direct influences. The interaction effect of the first two (CC-SoC and price ratio) is also influential. However, this leads to the second key issue: interpretability. Although high prediction accuracy (87.1%) was achieved, the black-box nature of the approach limits its policy relevance. As such, this research applied a technique, Accumulated Local Effects (ALE), that can determine the strength and direction of influence of the variable. This research demonstrates how machine learning can be applied to a policy-relevant question and provide information that is useful to policy decision makers.

A critical aspect of climate policy is effective communication about greenhouse gas emissions (GHG-E) themselves. However, since attitudes towards climate change vary by different segments of the population, different methods of communication about GHG-E may be necessary. This study investigates different responses to GHG-E information by population and how to best communicate such information with them. Eight framing techniques following goal framing theory and moral foundations theory were developed, and a discrete choice experiment was applied to determine willingness to pay (WTP) for CO2 emissions reductions. Six segmentation analyses for people's WTP for emissions under different framing techniques were carried out for culture, region, urban-rural residence, political leaning, attitudes and beliefs towards climate change, and income. Based on the data of 2015 respondents from six provinces of Canada, the strength of GHG information influence was tested according to different respondent segmentations. The results show that in most cases, an emoticon framing has the strongest influence, though two exceptions exist: With respect to political orientation, nearly all framings were ineffective for those on the extremes, and for those on the right (not extreme) the current label has the largest impact; With respect to climate change motivation, color framing works best for those unsure how to reduce emissions, while the current label works for those uninterested in reducing their emissions. Surprisingly, "tree" framing was most effective among those who report not being concerned about climate change. The results help policy and decision-makers improve the likelihood of climate-friendly choices being made.

Various measures have been proposed and validated to assess environmental motivation and explain peoples' consumer behavior. However, most of the measures are rather complex, sometimes comprising dozens of items. In order to overcome the associated response burden, the goal of our research is to validate a much simpler measure of environmental motivation, namely the measure of Climate Change-Stage of Change. To do so we analyze data from a discrete choice experiment in which drivers decide to purchase a car with different levels of CO2 emissions and we also measure their environmental motivation with three alternative measures. The results show that environmental motivation assessed with Climate Change-Stage of Change explains the choices in the experiment as well as with more complex measures. Our findings have substantial implications for researchers as they may be able to assess climate-relevant motivation – a significant factor for many consumer choices – with a single question.

Shared mobility is becoming increasingly popular worldwide, and travelers show more complex choice preferences during the post-pandemic era. This study explored the role of shared mobility in the context of coronavirus disease (COVID-19) by comparing the travel mode choice behavior with and without shared mobility. Considering the shared mobility services of ride-hailing, ride-sharing, car-sharing, and bike-sharing, the stated preference survey was designed, and the mixed logit model with panel data was applied. The results show that if shared mobility is absent, approximately 50% of motorized mobility users and 84.62% of bike-sharing adopters will switch to using private car and public transport, respectively. The perceived pandemic severity positively affects the usage of car-sharing and bike-sharing, while it negatively affects the ride-sharing usage. Under different pandemic severity levels, the average probabilities of private car choice with and without shared mobility are 38.70 and 57.77%, respectively; thus, shared mobility would alleviate the dependence on private car in post-pandemic future. It also helps to decrease the on-road carbon emissions when the pandemic severity is lower than 53. These findings suggest policymakers to maintain the shared mobility ridership and simultaneously contain the pandemic. Additionally, pricing discount and safety enhancement are more effective than reducing detour time to protect ride-sharing against COVID-19.

This study aims to develop better framing techniques for presenting greenhouse gas emissions information. To improve on previous research, two new hedonic framings (new emoticons, tree scale), one injunctive norm framing (thumbs up/down), one visual framing (pressure gauge), and two moral foundation framings (patriotic goal and dirty air scale) are introduced and applied to the current Natural Resources Canada vehicle labels. Discrete choice experiments were used to evaluate the effectiveness of different framing techniques represented by the willingness to pay (WTP) for emissions. The survey region is extended to six provinces of Canada and two subsamples were tested: car owners, those who plan to own a vehicle. Of the frames tested, the new emoticon resulted in the greatest increase in WTP. Those who intend to buy were not as strongly influenced by the framings. The upper three stages of climate change concerns have a positive WTP irrespective of the framings.