Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country

Investigating the role of green behavior and perceived benefits in shaping green car buying behavior with environmental awareness as a moderator

As the planet faces the challenge of global warming, every individual and organization must adopt green practices to protect nature. The automobile industry is one of the primary industries which can contribute significantly towards sustainability. This study aims to examine the impact of green behavior and green perceived benefits on the green buying behaviors of automobiles. The research also explores the moderating influence of environmental awareness on the mechanism. The research is based on a quantitative method for which primary data was gathered from 406 respondents across Pakistan, China and Saudi Arabia via Quota-based purposive sampling. The gathered data was analyzed via SmartPLS. The results show that green behavior and perceived benefits positively and significantly influence green buying behavior. The findings also show the moderating role of environmental awareness on green behavior towards green buying and show no impact on the perceived benefits towards buying behavior. The study has practical and theoretical implications for managers, researchers, policymakers and institutions in the context of green automobile development and businesses. The study also contributes to the attainment of sustainable development goals.

Estimation of Shipping Emissions in Developing Country: A Case Study of Mohammad Bin Qasim Port, Pakistan

Transportation has the highest dependence on fossil fuels of any sector and accounts for 37% of carbon dioxide (CO₂) emissions. Maritime transportation is responsible for around 940 million tons of CO₂ and approximately 3% of global emissions annually. The significant increase in shipping activities around the globe has magnified the generation of toxic pollutants. In recent years, shipping emissions have received significant attention in developed countries due to global climate change, while in developing countries, researchers are making enormous efforts to tackle this catastrophic and pressing issue. This study considers Muhammad Bin Qasim Port (MBQP), Karachi, Pakistan as a case study. This study employed an activity-based or bottom-up approach with a standard procedure to estimate the various anthropogenic pollutants emissions including particular matters (PM₁₀ and PM_2.5), nitrogen oxide (NO_x), sulfur dioxide (SO₂), carbon monoxide (CO), CO₂, methane (CH₄), non-methane volatile organic compound (NMVOC), and hydrocarbon (HC) under different operational modes, i.e., hoteling, maneuvering, and reduced speed zones. The results indicated that CO₂ was the highest contributor with a proportion of 92%, NO_x 5%, and SO₂ 1.5% for all three operational modes. Moreover, the results indicated that container ships account for 64% of overall emissions, followed by tankers for 24%. Regarding the monthly trend, the findings revealed that November and December had the highest emission rates, with over 20% of the total emissions recorded. This study's findings will assist stakeholders and policymakers to prioritize maritime emissions in developing countries.

Analyzing Spatial Location Preference of Urban Activities with Mode-Dependent Accessibility Using Integrated Land Use–Transport Models

Accessibility is the ease of reaching opportunities (goods, services, activities, and destinations). Accessibility of desirable locations such as households and commercial locations, is typically scaffolded by land use patterns and transportation infrastructure. It can reflect people’s travel convenience, cities’ viability, sustainability, and mitigate the negative effects on the environment and public safety. Consequently, it is recognized as a fundamental principle in urban sustainable development policies worldwide. In the literature, most of the studies have used a static or partially dynamic approach with a single mode such as a car or public transportation by using conventional models. These “static” models assume that household locations are static and that transportation supply and opportunities for social practice activities are fixed in time and space, which can lead to biased or even misleading assumptions in accessibility models. Therefore, the aim of this study is to evaluate the impact of dynamic spatial accessibility through Mode-Dependent Accessibility (MDA) on the location choice behaviors of urban activities such as households and commercialin the City of Wuhan, China. This study employed the Mode-Dependent Travel Demand Model (M-TDM) to measure the impact of short-term MDA on household and commercial activities for the years 2012 and 2015. Additionally, an integrated spatial economic (ISE) model such as PECAS (Production, Exchange, Consumption, Allocation, System) in order to investigate location preferences of urban activities over space and time. Regarding household and commercial location choice, the ISE modeling results revealed that households and commercial activities are sensitive to MDA, especially using transit. The ISE method predicted that the R2 for household and commercial location choice models was 0.84 to 0.90 for transit-based accessibility, whereas the R2 for logsum-based static models was 0.48 to 0.72. In addition, their findings suggest that highly accessible locations that are well served by auto are more appealing for household and commercial activities. The findings of this study will help urban planners, transportation planners, and policymakers take into account the dynamic nature of short-term MDA when zoning and allocating urban activities and public amenities, instead of using static accessibility.

A Fuzzy-Logic Approach Based on Driver Decision-Making Behavior Modeling and Simulation

The present study proposes a decision-making model based on different models of driver behavior, aiming to ensure integration between road safety and crash reduction based on an examination of speed limitations under weather conditions. The present study investigated differences in road safety attitude, driver behavior, and weather conditions I-69 in Flint, Genesee County, Michigan, using the fuzzy logic approach. A questionnaire-based survey was conducted among a sample of Singaporean (n = 100) professional drivers. Safety level was assessed in relation to speed limits to determine whether the proposed speed limit contributed to a risky or safe situation. The experimental results show that the speed limits investigated on different roads/in different weather were based on the participants’ responses. The participants could increase or keep their current speed limit or reduce their speed limit a little or significantly. The study results were used to determine the speed limits needed on different roads/in different weather to reduce the number of crashes and to implement safe driving conditions based on the weather. Changing the speed limit from 80 mph to 70 mph reduced the number of crashes occurring under wet road conditions. According to the results of the fuzzy logic study algorithm, a driver’s emotions can predict outputs. For this study, the fuzzy logic algorithm evaluated drivers’ emotions according to the relation between the weather/road condition and the speed limit. The fuzzy logic would contribute to assessing a powerful feature of human control. The fuzzy logic algorithm can explain smooth relationships between the input and output. The input–output relationship estimated by fuzzy logic was used to understand differences in drivers’ feelings in varying road/weather conditions at different speed limits.

Exploring the Effects of Transportation Supply on Mixed Land-Use at the Parcel Level

The interactive relationship between transportation and land use has become more difficult to understand and predict, due to the economic boom and corresponding fast-paced proliferation of private transportation and land-development activities. A lack of coordination between transportation and land-use planning has created an imbalanced provision of transportation infrastructure and land-use patterns; this is indicated by places where a high-density land-development pattern is supported by a low-capacity transport system or vice versa. With this, literature suggests that Mixed Land-Use (MLU) developments have the potential to provide relevant solutions for urban sustainability, smart growth, inclusive public transit use, and efficient land-use. Therefore, this study employed deep neural network models—Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP)—for forecasting the effect of transportation supply on the MLU pattern at the parcel level in the Jiang’an District, Wuhan, China. The findings revealed a strong relationship between the supply of public transportation and MLU. Moreover, the study results indicated that MLU is widely available in areas with high accessibility, high density, and proximity to the city center. The forecasting results from the MLP and LSTM models showed an average error of 5.55–7.36% and 3.62–4.28% for mixed use, respectively, while most of their 90th percentile errors were less than 13.73% and 10.46% for mixed use, respectively. The proposed models and the findings from this study should be useful for stakeholders and policy makers for more precise forecasting of MLU at the urban level.