Search

• Car sharing is unevenly distributed in the Oslo region and within the municipality. • Ares with middle income and higher education levels scores higher on car sharing. • Car sharing is more prominent in areas with high share of cultural industry workers. • It is more frequent in areas with higher shares of men and multifamily housing. • Car sharing correlates with areas scoring high on public transport competitiveness. This paper examines urban patterns of car sharing supply and demand at neighbourhood level. By using literature on spatial inequality and unequal mobility access, we provide new insights into the urban geographies of car sharing. We draw on register data of members and vehicles of station-based car sharing for Oslo, Norway, spatially coupled with population composition, urban form and public transport accessibility ratios. We find both the demand and supply of station-based car sharing to be unevenly distributed. Some of the most profound contributors to car sharing demand include middle income levels, middle and higher levels of education, higher shares of the population working in cultural sectors, higher shares of men, higher shares of multifamily housing, and higher strengths of the public transport regime compared to that of the private car. The paper discusses the implications of these findings for sustainable urban mobility, mobility justice and urban and transport planning. [ABSTRACT FROM AUTHOR]

• Provides method for analyzing areas of high parking demand. • Identifies e-scooter vendor launching sites. • Compares techniques for analyzing roadway segments of high micromobility demand. • Discusses policy implications and potential environmental impacts of micromobility. • Examines the accuracy of shortest path models in predicting e-scooter paths. To enhance the effectiveness of dockless shared micromobility vehicle services, address challenges created by these services, and achieve better implementation performance, cities need efficacious strategies to tackle two major challenges: parking demand and infrastructure improvements. This paper offers two techniques for analyzing areas of high parking demand and roadway segments of high micromobility vehicle demand. The proposed processes support efforts to assign free-floating parking and reduce vehicle clutter across cities, and assist transportation planners in identifying locations for road infrastructure improvement and ordinance enforcement. An unsupervised learning approach using vendor launching site analysis is used to appropriately locate dockless vehicle parking zones. Furthermore, this paper compares three methods for identifying high-demand corridors. Two shortest path models are generated to predict trip paths for trip data without trajectories, considering rider route preferences and infrastructure types. A Simplified Matching Heuristic (SMH) that uses trip data with trajectories, is developed herein to match trip trajectory data to the network street links. These analytical processes are applied in a case study of Dallas, TX. Results show that methods using shortest path models may poorly predict the actual path taken by e-scooter users. A discussion of policy implications for micromobility planning and management and potential environmental impacts of emerging transportation technologies are also presented. [ABSTRACT FROM AUTHOR]

• This paper provides a review of existing literature on CO 2 emission implications of HSR projects due to Modal Interactions. • We consider the interactions of HSR with other transport modes, including air, road, and conventional rail. • We conceptually classify the related studies into three levels. • Most papers on the HSR-induced modal shift and CO 2 emissions focus on air transport. • We identify future research opportunities after the review. This paper reviews existing literature on the CO2 emissions implications of high-speed rail (HSR) projects due to its interactions with air, road, and ordinary-speed rail. We conceptually classify the studies into three levels, with the first focusing on the emission comparison between HSR and the other modes, the second on the emission implication due to HSR-induced traffic reallocation, and the third on the life cycle assessment (LCA). We find that the literature is still in a relatively premature state. We also identify future research opportunities, including: i) to carry out a more detailed and comprehensive analysis regarding how HSR affects cargo operations of other transport modes and the corresponding CO 2 emission implications, ii) to further develop the game-theoretical models to be applied to estimate a fuller picture of modal splits and the corresponding CO 2 emission impacts, and iii) to apply LCA by considering the avoided life-cycle emissions associated with infrastructure and vehicles due to traffic diversion and the intricacies of modal interactions. [ABSTRACT FROM AUTHOR]

• Research roadmap for pavement-EV interaction is proposed. • Three key pavement-EV interaction knowledge gaps are identified. • Eleven key urgent research questions are defined. The electrification of vehicle fleets will play a major role in reaching carbon neutrality by 2050. This paradigm shift for both passenger and heavy vehicles, coupled with changing mobility and freight trends, will require a change in current understanding of pavement design and management. This paper synthesizes the impacts of increasing electric vehicles (EVs) and mobility trends on pavements, in order to produce a 25-year research roadmap. From the literature review conducted, three principal pavement-EV interaction knowledge gaps were identified: 1) effect of EVs and upcoming mobility trends on pavement degradation, 2) pavement-EV energy loss mechanisms, and 3) effect of pavement on EV operating costs and battery life and associated environmental impacts. From the results of this study, a research roadmap is proposed to better understand these issues and develop solutions for the key challenges expected over the next 25 years. [ABSTRACT FROM AUTHOR]

This paper inspects the environmental Kuznets curve (EKC) in various transport sectors in 13 European Union (EU) countries. The study uses the augmented mean group estimator (AMG) estimator to examine the relationships between carbon (CO 2) emissions, income, and energy consumption of four transport modes, such as road, maritime, rail, and air, between 1995 and 2019. The outcomes illustrate that the EKC hypothesis is valid for six countries in air transport, four in maritime transport, three in rail transport, and six in road transport. In addition, the findings indicate that fossil fuels significantly increase CO 2 emissions from transportation modes. In light of the study's findings, EU countries should improve energy efficiency in the transportation sector to become carbon neutral by 2050. In order to ensure an environmental balance between transport modes, policymakers should support innovative activities to increase renewable fuel consumption. [ABSTRACT FROM AUTHOR]

In view of the climate emergency, it is necessary to model and forecast road transport emissions for implementing more effective climate policy. This paper fills an important research gap by quantifying the challenges, barriers and remedies of achieving road transport emission reduction and air quality improvement in developing countries. It contributes to the debate on top-down and bottom-up approaches to reconcile carbon and other transport related emissions. China and India are chosen as case studies. Using various official statistics and proxy variables, we estimate and compare the road transport carbon emissions of the two countries systematically from 2009 to 2020. Insights from detailed mode-specific estimations of the bottom-up approach are valuable for targeted policies and measures. An analysis of the major components of road transport carbon emissions points to strategies of promoting electric vehicles, reducing transport demand, accelerating modal shift to low-carbon modes, and cleaner electricity to achieve climate policy. [ABSTRACT FROM AUTHOR]

The implementation of Low Emission Zones (LEZs) is hampered by a lack of knowledge about social and economic impacts. This paper presents insights into how LEZs impact retail activity, innovatively including perspectives from both residents and retailers. A two-pronged research approach was implemented in Madrid, with two survey campaigns that analysed key drivers impacting residents' shopping activity and retailer sales levels within LEZs. The research design was based on discrete choice analyses. The main results indicate that private vehicle users with limited access to LEZ are expected to decrease their shopping activity within the LEZ. At the same time, inequalities in performance emerge between different types of stores, according to turnover and time at the location. The results provide policymakers with insights into more inclusive design and implementation of LEZ. [ABSTRACT FROM AUTHOR]

Previous studies on port environmental efficiency using DEA tended to be biased due to two main sources: (1) erroneous disposability assumption between desirable and undesirable outputs and (2) ignorance of data sampling bias. This study developed a robust novel methodology for measuring environmental efficiencies to correct for both estimation biases. First, to address the disposability bias between desirable and undesirable outputs, the weak disposability condition is imposed on the model-building process. Also, to solve inherent bias caused by the data sampling process, a bootstrapping method was adopted. Building on slacks-based measurement (SBM)-DEA models, a subsampling technique is adopted as a bootstrapping method. This paper suggests a novel approach to evaluating the volatility of the confidence intervals of the measured efficiency score. As a result of empirical study on the environmental efficiency of 9 Korean ports in 2019 ∼ 2021, we can examine the impact of environmental policies on port performance. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive eco-efficiency assessment for six promising long-life asphalt pavement (LLAP) designs as a structural system, including the full-depth (FD), thick asphalt layer (TAL), inverted (Ive), enhanced semi-rigid base (Esemi), composite (CP) and traditional semi-rigid base (Tsemi) asphalt pavement. Results showed that under the baseline design scenario, the Ive , ESemi and CP perform best in terms of greenhouse gas (GHG) emissions, total embodied energy (TEE), and life-cycle cost (LCC), respectively. In most cases of the scenario and uncertainty analyses, ESemi and Ive were the most eco-efficient solutions, although CP may dominate ESemi for a longer design life, whereas the other three designs were dominated by one or more of the abovementioned designs. Among all life-cycle activities, the raw material phase was proved to be the most critical source of GHG (54.2–79.4 %), TEE (41.8–54.9 %), and LCC (72.0–72.9 %), under the system boundary of cradle-to-grave (excluding operation phase). Striking a balance between the thickness of cement-based and asphalt structural layers is the key to achieving sustainable pavement. [ABSTRACT FROM AUTHOR]

Access to subway stations is important for daily commuting, but scant attention has been given to green space exposure at subway station areas in people's residential neighbourhoods and workplace areas. This paper focuses on the association between street-level green space exposure around subway stations at residential and work locations and people's choice of subway as their primary commuting mode and travel satisfaction, using street view data and survey data in Beijing, China. Street view data and a machine learning approach were used to measure both street view green space quantity (SVG-quantity) and street view green space quality (SVG-quality). The results suggested that SVG-quantity and SVG-quality generate differential effects on subway use and travel satisfaction under residential and workplace contexts. Findings of this study highlight the complementary effects of green space and travel infrastructure provision in shaping travel behaviour and wellbeing in residential neighbourhood and workplace contexts. [ABSTRACT FROM AUTHOR]

Road freight emissions form a significant part of a country's total emissions. Thus, one of the key steps for achieving net zero is to decarbonise road freight. Battery electric heavy goods vehicles (HGVs) require large batteries that are heavier and more expensive. This paper investigates the feasibility and impact of electrifying HGVs using electric road systems (ERS), thus reducing the need for large batteries. It is established that using the ERS reduces well-to-wheel (WTW) emissions by approximately 10%. A cost breakeven analysis was formulated to identify the locations where an ERS is economically viable in England, France, India and South Africa. The differences in these countries are observed in terms of their road freight distribution. This analysis reveals that up to 47% of the total road freight in England, 72% in France, 38% in India and 57% in South Africa could be electrified using ERS with a 20-year breakeven period. [ABSTRACT FROM AUTHOR]

Energy consumption management is a referential approach to alleviate range anxiety. In this paper, the data-driven modeling and analysis of energy consumption based on low-frequency real-world electric bus data is implemented. Specifically, two quantitative metrics of energy consumption are identified and calculated regarding to the characteristics of low-frequency real-world data. Also, the Road-Driver-Vehicle-Ambient factors system is proposed, forming a comprehensive digital description of the driving process. Supervised learning models with different levels of complexity are then established to predict the energy consumption, with the mean absolute percentage error of 9.2%, 7.5% and 7.7% respectively. Last but not least, the relationship between each factor and energy consumption is explained through quantitative, qualitative and statistical analysis, which intuitively shows the influence of Temperature, Driver/Vehicle-related and Road-related factors on energy consumption. [ABSTRACT FROM AUTHOR]

• Leaching characteristics of porous asphalt materials were examined. • Acid rain affects the short-term leaching behavior of porous asphalt mixture. • Biochar filler has a greater effect on the leaching of total nitrogen. • Adverse effects are found from leachate under long-term soaking. • The leaching extent of porous asphalt can be determined after testing for 7 days. Permeable pavement leachate can be a potential issue as contained pollutants are washed off by rainfall. This paper investigated the leaching of porous asphalt mixture (PA) with different fillers via static tank leaching test. The effects of PA leachate on zebrafish were explored. The results show that within 1–2 days, the leaching of alkali, total nitrogen (TN), and total phosphorus (TP) fluctuates greatly, with acidic rain accelerating TN leaching. After long-term soaking, the pH of most leachate stabilizes at 8, and the leaching amounts of TN and TP in acidic leachant are 0.2 mg/kg and 0.04 mg/kg, respectively. PA with biochar leaches higher TN in deionized water (DI). Short-term leachate has little effect on zebrafish, while attention is needed when the damage degree or the Nemerow pollution index exceeds 5. Static tank leaching test is suitable for monolithic PA specimens, and leaching can be determined after 7d soaking in DI. [ABSTRACT FROM AUTHOR]

With the rapid development of charging-while-driving technology, the deployments of wireless charging lanes will inevitably affect the route choice of Battery Electric Vehicle (BEV) drivers and the power supply of power grids. This paper builds a bi-level optimization problem to optimize the locations of charging lanes and charging stations given an integrated transportation-power network. The decision variables of the upper level include the locations of charging lanes and charging stations and the electricity generated in each busbar (an electricity generation node in the power network), while the lower level determines the charging strategy and the route choice of the drivers. Then we develop an effective solving algorithm for the problem through various reformulations. The results from the numerical study and sensitivity analysis show that charging infrastructures' deployment affects the route choices of BEV drivers significantly, and charging lanes can be a more economical and effective charging method than charging stations. • Integrated location optimization of wireless charging lanes and fast-charging stations. • A bi-level optimization framework considering traffic congestion for deploying charging infrastructure. • Mutual impacts between the power grid network and the transportation network are captured. • An effective algorithm is proposed to solve the non-convex bi-level optimization problem. [ABSTRACT FROM AUTHOR]

The paper investigates the features of emissions and fuel consumption (EFC) in a car-following (CF) platoon based on two experimental datasets. Four classical EFC models are employed and a universal concave growth pattern of the EFC along a platoon has been demonstrated. A general framework of coupling EFC and CF models is tested by calibrating and simulating three classical CF models. This work first demonstrates that, at vehicle-pair level, all models perform well on EFC prediction. The intelligent driver model outperforms the other CF models on calibration accuracy, but this is not true on EFC prediction. Second, at platoon level, the predicted EFC is nearly constant along the platoon which qualitatively differs from the experimental observation. The investigation highlights that accurate estimations at vehicle level may be insufficient for analysis at platoon level due to the significant role of oscillation growth and evolution in EFC estimation. [ABSTRACT FROM AUTHOR]

Many airports face challenges with slot scheduling and noise pollution. While previous research has focused on optimizing slot allocation to mitigate noise in the temporal dimension through schedule displacement, little attention has been given to the spatial dimension. This paper expands the environmental benefits analysis of slot allocation from the temporal dimension to the temporal-spatial dimension. To this end, we develop a single-airport bi-objective slot allocation model that considers schedule efficiency and noise reduction through schedule displacement (temporal dimension) and takeoff direction changes (spatial dimension). Then, we compare the efficacy of takeoff direction changes for noise reduction with that of schedule displacement. Additionally, we investigate the effects of takeoff direction changes on schedule displacement. Results show that takeoff direction changes are more effective than schedule displacement for noise abatement at Shanghai Hongqiao International Airport. Moreover, takeoff direction changes can influence slot displacement allocated to each airline due to fairness considerations. [ABSTRACT FROM AUTHOR]

The acceleration of global e-commerce brings an increasing environmental burden to urban last-mile logistics. Autonomous delivery robots (ADRs) have often been considered as an attractive solution to this challenge but, to date, their environmental impact had not been fully assessed. To fill this gap, a life-cycle analysis of two-echelon and business-as-usual distribution strategies is proposed in this paper. To model ADR production, primary data from an actual prototype is used. The mathematical formulation of the use stage is done using the continuous approximation methodology. Finally, some managerial insights are obtained. Two-echelon operations would generate between 60 and 130 gCO 2 -eq per parcel delivery depending on the considered operation scenario. The ADR fleet production and renewal are the biggest contributors to this total global warming potential (GWP). As a consequence, the three main leverages to decrease the GWP of an ADR-based two-echelon delivery scheme are an improvement of the ADR production processes, the maximization of the robot lifespan (both for mechanical parts and battery), and the optimization of delivery operations to minimize the robot fleet size. [ABSTRACT FROM AUTHOR]

Benchmarking the energy efficiency of ships is not a straightforward task, mainly due to the diversity of operations. Although driving cycles have been used for decades in evaluating the performance of road vehicles, these do not exist in formal policy-making for maritime transport. This work builds on a previously proposed methodology. It uses noon reports of 327 vessels for 2019 to construct operational cycles for seven size classes of container ships using the main engine power as the main parameter. Concerning the main engine emissions, the resulting cycles reduce variation in the carbon intensity indicator values by more than 30% while maintaining an average accuracy of 97.7% in absolute emissions. These figures show that the concept can improve operational carbon intensity indicators in terms of robustness and their technical counterparts in optimizing ship design. The paper also proposes further work required for benchmarking applications in policy-making. [ABSTRACT FROM AUTHOR]

Dynamic wireless charging (DWC) technology has the potential to promote the uptake of electric freight vehicles (EFVs) by allowing charging on the move. This paper presented a joint DWC lane deployment and operation model based on the trajectory data of actual EFVs. The model was formulated as a multi-objective mixed-integer programming model, with objectives to maximize saved charging time, minimize charging cost, and minimize negative impact on traffic. The model considered various constraints related to investment, facility construction, and charging decisions of EFV drivers. It was first tested on a small network and was further applied into a large-scale scenario in Beijing. The results suggested that DWC could reduce charging time by approximately 0.08–0.11 h per EFV per day within an investment limit of 67 million dollars. The deployment of DWC can be long-term profitable, resulting in a net value of 140.83 million dollars over a 25-year period. [ABSTRACT FROM AUTHOR]

In this paper we present and test a survey instrument to determine the prevalence and severity of travel problems, defined as any difficulty a person may experience in reaching destinations due to a poorly functioning transport system. The tool distinguishes between three types of travel problems: difficulties encountered while traveling; reliance on others; and forgoing trips. The deployment of the tool prior to and during the COVID-19 pandemic offers a unique natural experiment. A series of tests for internal validity, consistency, and reliability, including confirmatory factor analysis, support two of the proposed travel problem indices (Reliance and Trips Forgone). Exploiting the natural experiment, known-group analyses and Two-Part regressions delivered evidence for the external validity of the indices and demonstrated the ability of the instrument to elicit insights on travel problems. We end with a research agenda for developing robust measurement tools that can be used in research and policymaking. [ABSTRACT FROM AUTHOR]

Reduced headways of connected and automated vehicles (CAV) provide opportunities to address traffic congestion and environmental adversities. This benefit can be utilized by deploying CAV-dedicated lanes (CAVDL). This paper presents a bi-level optimization model that captures CAV market size uncertainty. The upper level determines the links (and number of lanes) for CAVDL deployment to minimize emissions. It considers lane reallocation policies that account for the prospect of smaller width of CAV-dedicated lane due to the smaller lateral wander of CAV tire tracks. This can increase the total number of lanes on wide highway sections. At the lower level, equilibrium and demand diffusion models capture travelers' route and vehicle-type choices. The bi-level model is formulated as a min–max mathematical program with equilibrium conditions and solved using the cutting-plane scheme and active-set algorithm. The computational experiments indicate that the robust plans have superior performance compared to the deterministic plan in pessimistic cases. [ABSTRACT FROM AUTHOR]

While a growing number of studies have explored evacuation expenditure for more effective evacuation planning and emergency management, few have investigated the public preferences regarding hurricane evacuation behavior and the cost of evacuation. This paper presents an empirical analysis of hurricane evacuation decisions, using both revealed preference (RP) and stated preference (SP) evacuation expenditure data from Texas residents affected by Hurricane Ike. Furthermore, we have developed a methodology framework based on utility maximization theory and the lifecycle consumption model. This approach allows us to estimate the lower bound of individuals' willingness to pay (WTP) to mitigate hurricane risks and calculate the associated cost-of-evacuation under both voluntary and mandatory evacuation orders. The study was carried out from a transportation cost perspective and included the expenditure of transportation, food, and lodging during hurricane evacuation. The findings provide policy implications for emergency management agencies in coastal communities in terms of hurricane evacuation planning and strengthening disaster management practices in the future. [ABSTRACT FROM AUTHOR]

The development of BEVs (battery electric vehicles) can significantly mitigate the dependence on fossil fuels and reduce carbon emissions. To advance the development of BEVs, China is adjusting the subsidy and dual-credit policies. This paper addresses these two issues to help policymakers determine effective policies. Through the simulation with a system dynamics model that incorporates the government, automakers, infrastructure operators, and consumers, we find that (1) reducing the overall level of subsidies has a minor impact on the development of BEVs in the long term; (2) subsiding the construction of infrastructure, especially battery swapping stations, will produce better effects than others; (3) the proposed carbon quota mechanism, which is in line with the unified carbon market, is more conducive to the diffusion of BEVs and carbon reduction than the dual-credit policy. Our MLP-SD (multi-level perspective system dynamics) model broadens the analytical perspective of the technology transition in the automobile industry. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic Driver-Pressure-State-Impact-Response (DPSIR) method for traffic performance assessment under urban floods caused by extreme rainfall events. A coupled rainfall-flood-traffic model has been developed as the prerequisite for evaluation with publicly available data. The DPSIR method integrates the natural and transportation systems into cause-effect-response impact chains and facilitates dynamic traffic performance measurement on the road and network levels. We apply this approach to the urban networks' motorways, trunk, and arterial roads in Shanghai, China. The results help identify critical roads and verify the effectiveness of demand-control strategies in improving traffic resilience. By offering decision-makers a comprehensive view of traffic operation under disruptions, this method can help them build a more resilient transport system in flood risk management. [ABSTRACT FROM AUTHOR]

Governments consider electric vehicle (EV) adoption as one of the strategies for reducing the negative impacts of the transport sector. However, the assessments that are usually conducted do not consider the impact that this transition may have on other aspects of the transport and energy sectors. This paper presents a system dynamics model to evaluate the effects that policies associated with EV adoption have on sustainability indicators, as well as on the vehicle and energy supply chains in Colombia. The main findings show that the proposed fiscal measures do not achieve massification in the medium term and that the capacity of the energy system may be a limiting factor in the adoption process. Systemic policy evaluation identifies problems that may emerge in their implementation, such as limiting factors and that policy efforts are not leverage points. It would, therefore, allow for better strategies to be formulated. [ABSTRACT FROM AUTHOR]

Given the rapid rise of remote work, there is an opportunity for new shared mobility services designed to meet the needs of passengers with multiple possible work locations. This paper develops a new optimization model to enable shared mobility systems to match drivers and passengers when passengers have flexible destinations. Constraints representing employer policies, such as mandatory co-location of colleagues and limited capacity of satellite offices are introduced in order to explore the impact of employer remote work policies on travel demand. A case study using historical demand data demonstrates that incorporating flexible work locations can increase ride-pooling participation by up to 6.7% and reduce vehicle-kilometers traveled by 4.9%. Outcomes are found to be significantly affected by employer policies. The implications of the results for shared mobility business models, employer remote work plans and local transportation policy are discussed. [ABSTRACT FROM AUTHOR]

The sharp rise in popularity of micro-mobility poses significant challenges in terms of ensuring its safety, addressing its social impacts, mitigating its environmental effects, and designing its systems. Meanwhile, micro-mobility is characterised by its richness in passively generated big data that has considerable potential to address the challenges. Despite an increase in recent literature utilising passively generated micro-mobility data, knowledge and findings are fragmented, limiting the value of the data collected. To fill this gap, this article provides a timely review of how micro-mobility research and practice have exploited passively generated big data and its applications to address major challenges of micro-mobility. Despite its clear advantages in coverage, resolution, and the removal of human errors, passively generated big data needs to be handled with consideration of bias, inaccuracies, and privacy concerns. The paper also highlights areas requiring further research and provides new insights for safe, efficient, sustainable, and equitable micro-mobility. [ABSTRACT FROM AUTHOR]

Policy-makers are investigating ways to reduce greenhouse gas emissions of the transportation sector. Many government agencies have studied the effects of pavement type and characteristics on fuel consumption. Such models, valid for a particular set of simulation conditions, suggest that rigid pavements perform better than flexible pavements. However, it is unclear how this type of model can be applied at the scale of a realistic road network. This paper proposes a statistical approach to assess the relative importance of pavement type and characteristics on fuel consumption of heavy vehicle traffic by considering the specificities of meteorological conditions, heavy vehicles, and driver behaviours. A case study in Canada showed that the potential advantage of using rigid pavements was offset by cold climate effects and the consideration of a realistic statistical payload distribution. Road roughness accounted on average for 1.1% of total fuel consumption, with an increased value of 1.9% in January. [ABSTRACT FROM AUTHOR]

In this paper, we examine the effect of various agreements between a socially concerned airport and an environmentally conscious airline regarding their profitability and channel coordination to decarbonize the aviation sector under two distinct settings. First, we consider no government interventions, and second, we explore government-imposed taxation to curb emissions. Our analysis shows that the revenue sharing and linear two-part tariff agreements coordinate the decentralized airport-airline channel. Our findings also reveal that under government interventions, taxation does help improve the greening level of the channel in both coordinating and non-coordinating agreements. However, coordinating agreements can achieve a higher greening level than simply imposing government taxation. Finally, we extended the model to include a duopoly airline market with pricing and greening competition. We analyze the effect of airline competition on airport utility, airline profit, ticket fare, and greening level. [ABSTRACT FROM AUTHOR]

• This paper evaluated advanced fuels and engines to lower emissions from yard tractors. • Emissions of diesel yard tractors have declined with tighter emissions standards. • Natural gas and LPG provide the potential for lower yard tractor emissions. • Diesel water emulsions provide reductions in both PM and NOx emissions. • Yard tractor emissions have declined >70% in NOx and >90% in PM at ports. Yard tractors (YTs) are trucks used for moving trailers and containers short distances around freight terminals and port facilities, and their diesel engines contribute significantly to nitric oxide (NOx) and particulate matter (PM) emissions to a port's inventory for the cargo handling equipment (CHE) category. Over time, tougher regulations have forced the introduction of new engine and emission control technologies to reduce emissions from YTs. This paper reviews the history and emissions from a range of technologies that were introduced during the past years with the understanding that a variety of technology solutions may be used by ports worldwide to address air pollution concerns. These new technologies included: cleaner engines, new liquid and gaseous fuels (natural gas [NG] and liquified petroleum gas [LPG]), and new emission control technology. The results show how technology improvements in YT emissions control technology have led to reductions of >70% in NOx and >90% in PM in the emissions inventories for CHE at ports in Southern California. [ABSTRACT FROM AUTHOR]

Transportation equity is a substantial concern for planners. Segregation and exposure analysis provide a lens from which community stakeholders can better decipher transportation equity challenges. This paper aims to expand racial segregation analysis beyond residential places to a more holistic activity space, including commuter populations. We filled existing research gaps on validity by calibrating Information Maximization (IM) model and a distance decay function to estimate race-specific driving patterns iteratively. A unique index of intergroup exposure and potential for contact between residents, workers, and commuters is proposed to understand the varying exposures different racial groups have with each other. We further identified the most racially-segregated road segments, residential and workplace areas, and how they become segregated based on the commuters' information. Given that exposure is a precursor to contact, understanding race-specific driving patterns is vital to understanding more extensive social mobility and segregation processes and their consequences for transport equity. [ABSTRACT FROM AUTHOR]

Studies on travel and life satisfaction in vulnerable Global South contexts are scarce. This paper contributes by addressing how travel satisfaction, neighborhood satisfaction, and social capital relate to life satisfaction among a low-income community in Bogotá (Colombia) as a case study. Building on the available literature, we propose six hypotheses to understand such relationships before and after a cable car implementation, considering control and treatment groups. We defined seven latent variables and three objective indicators. We tested the hypotheses using a multiple-cause multiple-indicator modeling approach, which allowed us to identify how the comprehensive cable car intervention impacts perceptions and life satisfaction. Results showed that the principal factors associated with life satisfaction were neighborhood satisfaction as well as leisure activity participation, while travel time and transport affordability had negative effects. Positive changes in satisfaction regarding the travel experience can trigger significant improvements in life satisfaction in low-income communities in Latin America. [ABSTRACT FROM AUTHOR]

[Display omitted] • Conducted life-cycle assessment of solar pavement. • PV system is the largest contributor to the environmental impacts of solar pavement. • Without considering power generation benefits, solar pavement has high emissions. • In long-term, the development of solar pavement is environmentally beneficial. The sustainable and green development of roads is lately receiving significant attention. As a multi-functional road capable of both accommodating traffic loads and generating clean energy, solar pavements exhibit a strategic opportunity for the future path of development in the road engineering. Although solar pavements have significant environmental benefits during their operation, they can lead to increased carbon emissions during the process of material preparation, construction, maintenance, etc. Therefore, it is essential to implement the framework of Life Cycle Assessment (LCA) to investigate their potential environmental impacts and/or benefits. Hence, this paper systematically analyzes the energy consumption and emissions of solar pavements based on LCA. The results show that the total energy consumption and gas emissions of the solar pavement are far greater than those of the traditional asphalt pavement. Nevertheless, in long-term the power generation function of the solar pavement has significant energy and environmental benefits. [ABSTRACT FROM AUTHOR]

Battery swap stations have become an important alternative to general charging posts. Predicting battery swapping demand at the station level would be helpful for real-time operation of stations. This paper first provided insights into battery swapping demand patterns by analyzing a real-world dataset which contained 2,529 battery swapping events collected from 36 battery swap stations in Beijing from 31st July to 20th August 2019. Further, we developed a series of deep learning methods to predict the EV battery swapping demand, particularly considering temporal demand patterns obtained from the dataset. The deep learning models were Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gated Recurrent Units, and Bidirectional Gated Recurrent Units. The results showed that the four deep learning models outperformed typical machine learning methods (e.g., support vector regression). An ablation study indicated that incorporating temporal battery swapping demand patterns into the deep learning methods could greatly improve model performance. [ABSTRACT FROM AUTHOR]

Currently, over 96% of the urban population is exposed to exceeding air pollution concentrations. Freight transport daily engenders €61,604 of air pollution health costs in the Brussels-Capital Region (BCR), of which 60% is incurred by vulnerable population segments. The construction sector is responsible for 26.40% of truck traffic in the BCR. This paper examines the exposure effects when off-site construction logistics flows are redirected around air pollution hotspots. Consequently, alternative routing scenarios are computed, and its emission dispersed assuming a Gaussian relation. Concentrations are then associated to spatiotemporal receptor densities. The health impact is monetized using hospital exposure-response functions. While overall emissions increase across all scenarios, health costs are mitigated up to 25.53% by rerouting existing flows. This study suggests to decouple policies from absolute transport emissions and focus on its health impact, considering spatiotemporal dynamics of both emissions and receptors. [ABSTRACT FROM AUTHOR]

The massive incorporation of light electric freight vehicles (LEFV) in the last mile distribution has been identified as one of the alternatives that large Latin American cities shall include in their agenda for cleaner and more effective public policy in logistics operations. Thence, in this paper, a routing optimization model was used to evaluate the reduction in operational cost and GHG emissions when fueled cargo trucks or motorcycles are replaced by LEFV (i.e., e-trikes or e-bikes) in the last mile distribution in large Latin American cities. Results showed that for the case of a commercial area in Bogota, and two lines of business (i.e., parcel delivery and dry chain), the use of LEFV reduces more than 50% de operational cost and more than 95% the GHG emissions. These high reductions are due to the synergistic effect of switching from fuel to electricity and reducing the energy consumption of vehicles. [ABSTRACT FROM AUTHOR]

The development of electric vehicles (EVs) is expected to play an important role in achieving the emission reduction targets in the transportation sector by many countries and regions, including China's dual carbon goals. A bottleneck to the mass roll-out of EVs is the limited charging facilities. This paper considers the planning of charging facilities for a new developing area in a metropolis, and an optimization model for charging station planning based on the dynamic transportation system is proposed. The proposed model is developed using an objective framework that considers the spatio-temporal characteristics of EV charging demand in order to minimize the overall cost while the constraints from suppliers and drivers are met. The Voronoi diagram is used to determine the final service boundary of each charging station, and the effect is verified in the planning of charge stations for the Yizhuang new town in Beijing. The case study confirms that the proposed method can optimize the charging facilities that fit well with the traffic network conditions. Furthermore, it is shown that the charging demand varies in accordance with the population density and regional functionality in different areas. [ABSTRACT FROM AUTHOR]

As a major carbon-emitting sector, transportation promotes regional linkages and triggers embodied CO 2 flows that accompany trade. This paper first constructs a 'provincial-national-global' multi-scale input–output (MSIO) model based on IO-nested technology. Then it evaluates the embodied CO 2 in transportation-related trade (TTEC) flows between Chinese provinces and Belt and Road Initiative (BRI) countries, and assesses their driving factors from multi-scale regional and cross-sectoral. Empirical findings show that regions with large TTEC flows are mainly concentrated in the northeastern, eastern and southern areas of China. The construction sector is the largest contributor to exported TTEC, while the energy sector has the largest imported TTEC. Singapore and India are the main exporting countries of TTEC from China, and India and Russia occupy a dominant position in importing TTEC. Finally, policy implications are put forward, that should provide quantitative and theoretical support for decision-making on reducing carbon in transportation between China and BRI regions. [ABSTRACT FROM AUTHOR]

UK has one of the world's largest automobile markets, with a growing share of alternative fuel vehicles (AFVs), and a great importance to the economy. However, an extensive insight into the price drivers of conventional vehicles (CVs) and AFVs is lacking in the literature. Therefore, this paper estimates the determinants of automobile prices, applying a hedonic pricing model to a novel, extensive dataset of 2008–19. Using adaptive Lasso and weighted quantile regressions, the key drivers of prices are performance and size, with the effect growing stronger with prices. More specifically, one standard deviation increase of engine power and weight increases prices by 16.9% and 14.7% respectively. AFVs display a significantly greater sensitivity to changes in car characteristics compared to CVs, especially to changes in performance, emissions, and equipment. Furthermore, hedonic price indices show that about 65% of the price increase in 2008–19 was caused solely by improvements in vehicle quality. • Hedonic pricing model is applied to a new and extensive UK car market dataset. • Key drivers of prices are performance and size, with the effect growing with price. • One standard deviation rise in engine power & weight rises price by 16.9% & 14.7%. • AFV prices significantly more sensitive, especially to performance and emissions. • About 65% of the price increase in 2008–2019 caused by improvements to car quality. [ABSTRACT FROM AUTHOR]

The paper contributes to the literature on the Environmental Kuznets Curve providing an empirical analysis of the relationship between air pollutant emissions from motor vehicles and social and economic factors in 56 major Russian cities using a panel data set for 2013–2018. The fixed effect regression models were used to analyze the impact of gross municipal product per capita on various types of automobile emissions. We found the evidence of an N-shaped Environmental Kuznets Curve relationship: emissions rise with economic growth due to the rapid automobilization, thereafter growth slows down, giving way to a decline. The reason may be that the households and commercial organizations in wealthier cities can buy newer cars, which correspondingly meet modern ecological standards and require cleaner fuel. Then, beyond a certain point of the gross municipal product per capita, the growth of automobile emissions resumes. We argue that the further decline is due to the structural economic dynamics and technological shifts, and the second ascending part is because the scale effect outperforms all the others. Other substantial results are as follows: (i) population densification leads to increased automobile pollution in large cities, whereas in relatively small municipalities, the effect is opposite; (iii) roads density is a significant factor of emission reduction in large cities; (iv) increase in the cost of gasoline leads to less vehicular emission. Our results emphasize that Russian cities need massive investments in transport infrastructure to form a new settlement framework outside the established centers. This will not only significantly affect the problem of atmospheric air pollution, but also improve the overall quality of life. Finally, it is necessary to increase the transparency of state environmental statistics, including by expanding the network of monitoring of atmospheric pollution in cities. [ABSTRACT FROM AUTHOR]

While electric vehicle (EV) use has proliferated in developed countries, emerging nations have lagged behind because they lack the resources to establish effective EV charging networks. To address this issue, this paper has developed a framework through scenario analysis that aims to minimize the costs involved in establishing such networks and provide policy makers with insights on how to accomplish this. A given set of public institutions possessing EV fleets were first grouped into zones using clustering algorithms. Mathematical models were then developed to identify optimal EV charging station locations serving the EV fleets of these organizations, with an objective to minimize the total cost. Factors such as EV range anxiety and human walking distance were imbedded into the framework, which was examined using real world data from three major cities in Morocco: Rabat, Casablanca, and Fes. With the framework, policy makers can make better planning decisions on EV transitioning. [ABSTRACT FROM AUTHOR]

This paper explores a cost-efficient resource assignment for urban battery-electric-buses (BEBs) considering decision robustness based on the optimized charging plan. A network flow model was proposed to minimize the operation cost with mixed fleet, nonlinear charging and the constraint of charging station capacity. A sensitivity analysis was implemented using samples generated from clustered real-world data to simulate traffic-dependent bus travel time and energy consumption. The result shows that charging at night or with short duration in the daytime significantly increases the operation cost. Sufficient charging resources improve the robustness of the charging plan, while economic benefits merely increase when the charging station capacity reaches a certain threshold. Bus operators can reduce the investment in chargers to ensure both economic benefits and operation stability. When bus routes are longer with higher energy consumption, buses with medium or high battery capacity become more demanding, while the requirement on charging resources remains stable. • An electric bus scheduling method considering resources limitation is proposed. • Charging strategy, resource allocation and fleet composition are optimized. • Trip consumption uncertainty is simulated by random samples from real-world data. • Buses with small or medium battery sizes can serve the network optimally. • Economic benefits do not increase when charging resources are sufficient. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology in traffic noise assessment, whose objective is to combine microscopic traffic simulations and noise calculation methods with macro-level, systemic noise impact assessment models. This combination, referred to as the vehicle-specific noise exposure cost (NEC), provides a per-vehicle contribution to the overall noise impact. Three case studies are introduced illustrating the potential of the methodology: a reference case with a dynamic traffic flow, the correlation between vehicle-specific NECs and average speeds, and vehicle-specific NECs in a mixed traffic fleet. The results highlight the interest and importance of using a microscopic approach, as the impact of interactions, vehicle-specific characteristics and behaviors are reflected into the associated NECs. Additionally, the correlation between vehicle-specific NECs and average speeds strongly depends on traffic conditions, further highlighting the importance of methodological features such as the interactions captured in microscopic traffic simulations or the acceleration-dependency of the implemented vehicle noise source model. • Original approach for noise impact allocation combining micro and macroscopic models. • Evaluation of microscopic noise impact while preserving the systemic impact. • Accounting for vehicle interactions and changing traffic conditions. • Application to academic test cases with mixed fleet and varying traffic density. [ABSTRACT FROM AUTHOR]

Increasing shipping traffic in the Arctic Ocean creates an emerging need to understand the consequences of maritime operations on the Arctic environment and coastal Indigenous and non-Indigenous communities, as well as potential governance responses. To address this need, we examine recent shipping trends and assess their impact on Arctic environments and communities. Our arguments are novel, and are built around contemporary empirical investigations and published scientific studies, reports, and government documents. The paper concludes that the environmental and community impacts vary across the Arctic and that greater international coordination is needed to learn from experience, to share assets and capacities, and to guide responsible and sustainable development of Arctic shipping. Given the possibility for opening of the Transpolar Sea Route within the coming decades, further proactive steps, such as developing a governance framework, could help Arctic shipping avoid rather than attempt to correct problems. [ABSTRACT FROM AUTHOR]

Paratransit, in particular the minibus taxi, is the mainstay of public transport in sub-Saharan Africa. These vehicles are often second-hand, ageing, fuel inefficient, and expensive to operate - issues that electrification can ameliorate. However, modeling and planning large-scale transitions to electric paratransit require reliable estimates of vehicle energy consumption. This paper provides such estimates by applying a vehicle kinetic model to per-second GPS data gathered on minibus taxis. Data include 62 trips across three routes with different driving conditions near Stellenbosch, South Africa. We find a range of energy consumption from 0.29 to 0.51 kWh/km (mean = 0.39 kWh/km). Past estimates in literature relied on per-minute GPS data, which we show leads to inaccurate energy consumption estimates. We recommend new kWh/km values for modeling vehicle operations and grid impact, and discuss how future work can utilize our analysis to advance the transition to electric paratransit sub-Saharan Africa. [Display omitted] • Highest fidelity kWh/km estimates for paratransit in sub-Saharan Africa to date. • Urban, hilly, and inter-city driving conditions are investigated. • 0.29–0.51 kWh/km (mean = 0.39 kWh/km), depending on driving condition. • Specific kWh/km recommendations for usage in operations planning and grid impact. • Per-minute data fails to capture effect of micro-mobility patterns on kWh/km. [ABSTRACT FROM AUTHOR]

Developing more walkable environments plays an essential role in healthy-city planning. Planners often assess the typical walkability framework from a geographic perspective based on GIS data. This paper proposes a refined walkability framework that quantifies walkability in terms of four pedestrian needs: safety, convenience, continuity, and attractiveness. Using Shenzhen as our case study, we integrate mesoscale and microscale built-environment features from different data sources: street view images (SVIs), social media, points-of-interest data, government open data, and GIS data. Deep learning semantic segmentation approaches apply to extracting street elements from SVIs. Results show that walkability and its four aspects are spatially heterogeneous in the city. Urban areas and suburban central business districts offer greater walkability than other areas. The less walkable areas are mainly in the rest of the suburbs. This work suggests that more academic and planning efforts should focus on improving walkability in less walkable neighborhoods and promoting pedestrian-friendly cities. [ABSTRACT FROM AUTHOR]

Charging station planning is critical in the implementation of public transport electrification. However, in cities, there is insufficient experience and knowledge of the factors that influence the utilization of charging stations, particularly charging stations aimed at serving a fleet of electric taxis. Shenzhen is one of the pioneers in promoting electric taxis. In this paper, we collect large-scale datasets from Shenzhen and provide a data-driven space–time analysis of the relationship between charging station utilization and urban form and demand for taxi services. We use a Random Forest Regression model to explore these relationships and apply a Shapley value method to interpret the results. We find that demand for taxi services, measured as hourly pick-up and drop-off densities, have a non-linear relationship with utilization. Metro station density positively correlates with utilization, whereas the relationships between population density, land-use entropy, road density, and bus station density are more complicated. [ABSTRACT FROM AUTHOR]

As an environmentally friendly transport mode, alternative fuel vehicles (AFVs) largely address environmental concerns by reducing harmful gas emissions. However, research on the pricing and advertising strategies of AFVs, notably the optimal level of commitment to advertising by AFV retailers, is still largely overlooked. Therefore, this paper explores the pricing and advertising strategies of CV (conventional vehicle) and AFV retailers when considering different advertising formats, namely, informative and persuasive, within a competitive market. We compare the profits of the two competitive retailers to arrive at the optimal decisions, as well as the investment levels of advertising. In addition to the effectiveness of the two stated advertising strategies, we derive some practical and counterintuitive results where for instance, the optimal price of the AFV retailer is positively correlated with the utility of the two types of advertising. Additionally, investment advertising does not necessarily bring benefits to AFV retailers. This study offers both valuable theoretical and practical contributions to the pricing and marketing of AFVs. [ABSTRACT FROM AUTHOR]

Using ammonia as an alternative marine fuel has been gaining interest in reducing greenhouse gas emissions in the maritime sector. This paper describes the development of a discrete event simulation model for bunker supply chain, emphasising how ammonia bunkering affects the operational and economic performance of the system. The results show that the model is a useful tool for ammonia bunker supply chain management, including the selection of the number and capacity of ammonia bunker supply vessels, bunkering flow rate and ammonia demand. Flow rate has a significant impact on bunkering service time, with an effect of up to 51.3% when it is changed by ±50%. The greater the demand for ammonia, the more significant the impact of ammonia bunkering flow rate on bunkering service time. Moreover, the number of ammonia bunker supply vessels is the most sensitive parameter for annual operational cost, with an effect of up to 15.2%. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid life cycle sustainability assessment (LCSA) model integrating multi region input–output analysis with novel multi-criteria decision-making techniques is proposed to assess three different fuel alternatives: compressed natural gas (CNG), electric buses (EBs), and diesel buses (DBs). A global hybrid LCSA model first quantified the environmental, economic, and social impacts of alternative fuel buses. The results were investigated in terms of multiple combinations of manufacturing and end-of-life scenarios by encompassing impacts embedded in the global supply chains taking Qatar as a case applied to the proposed model. The Interval-Valued Neutrosophic Fuzzy (IVNF)-Analytic Hierarchy Process with the Combined Compromise Solution (CoCoSo) approach is used to rank the alternative fuel buses based on their corresponding sustainability performance. The proposed model will help in quantitatively capturing the macro-level life cycle socioeconomic and environmental impacts along with optimally selecting alternatives to support sustainable urban transport policy towards a net-zero transportation system globally. [ABSTRACT FROM AUTHOR]