Izza Anwer

This paper focuses on the perspectives of passengers who were railway users and how railways as a service can be uplifted with technological advancements through the introduction of information and communication technologies (ICTs). For this purpose, a questionnaire was designed comprised of six sections related to information on socio-economic-demographics, travel, station facilities, train facilities, customer care, and familiarity with and benefits of ICTs. A total of 800 respondents were recruited on trains and in railway stations to collect data through a random sampling technique. Data were analyzed through descriptive statistics, factor analysis, bivariate correlation analysis, and ordered logistic regression analysis. The three hypotheses tested showed that (i) there is a correlation between socio-demographic factors, train frequency, and satisfaction levels, (ii) satisfaction with station and train facilities and customer care impacts users’ travel likelihood with the train service, and (iii) users’ familiarity with perceived benefits of ICTs influences passengers’ travel likelihood with the train service. The results indicate that the users’ satisfaction with attributes of station facilities, train facilities, and customer care and perceptions about ICTs significantly influences their travel frequency with the train service. This study is useful for multiple stakeholders, especially for railway management authorities, to provide inclusive services to passengers and to plan for future transportation, which should be well-equipped with ICTs, well-integrated with other transport modes, and well-connected with optimum stops.

Every country requires efficient public transportation to reduce the reliance on motor vehicles, decrease harmful emissions and noise pollution, and, most importantly, provide accessible transportation for urban populations with limited resources. Yet, several challenges hinder the efficiency of the public transportation system, affecting factors like daily ridership, infrastructure, revenue, and operations. Therefore, this research investigated barriers and their impact on the daily ridership of rail transit services, especially in densely populated areas characterized by high automobile usage, inadequate pedestrian facilities, and limited connectivity to other transportation modes. To accomplish this goal, a self-administered questionnaire was developed, and a survey was conducted to collect responses from commuters at various public transport hubs. Structural equation modeling (SEM) was used to analyze data gathered from a sample of 1000 participants. The findings from the SEM model indicated a positive correlation between Instrumental Attractiveness (IA) and Facility Design and Operation (FDO) with daily travel. Conversely, Service and Information (SI) along with Environmental Comfort (EC) exhibited a negative correlation with daily travel. Private sector employees without personal vehicles and a monthly income below fifty thousand exhibited a strong preference for public transport. Individuals with higher incomes who owned vehicles showed lower preferences towards public transport usage. The main contributors to low ridership in public transport included poor connectivity with metro and feeder buses, lack of availability of discount tickets, insufficient seating at stations, and inadequate route map information. Gasoline prices have a notable impact on daily ridership too, as higher fuel prices tend to attract more travelers to use public transport. The ridership data demonstrate a positive trend in response to increased gasoline prices. The findings from this study are useful for transportation engineers, practitioners, planners, and policymakers.

A sustainable information and communication flow (ICF) supports lifelines in floods, especially transport systems. A detailed insight into barriers regarding effective ICF through the implementation of information and communication technologies (ICTs) in the lifecycle of floods was given for evolved and evolving economies, i.e., York and Head-Marala, with sample sizes of 240 and 300 experts, respectively. All experts responded on an ethically approved questionnaire with further information notes that were used whilst discoursing the factors. ICTs were segregated into two groups, ranging from simple to advanced technologies. KMO and Bartlett’s tests confirmed high sampling adequacy with values ranging from 0.679 to 0.823 (≥0.5) with p-values ≤ 0.05. An amended version of Q-Methodology was used to identify nine factors in total. Each factor with an Eigenvalue ≥1 was retained, and all factors were highly reliable with values between 0.89 and 0.96. Factors were explained through communalities, factor loadings, pattern and structure matrices, and notes from experts. Results showed that under-evolved economies have limited technological resources and under-developed flood coping plans compared to evolved economies. Also, the unacknowledged possibility was uncovered that ICF can certainly be sustained if all possibly available ICTs are engaged through a thorough deployment plan of action. Authorities needed to make ICT engagement plans simple and efficient through effective coordination among different institutions. Though authorities were trained and equipped with modern tools, equipment, and technologies, dense and dependent populations overwhelmed the rescue capabilities. Other than VMS, social media pages, and radio, other ICTs were not tried and tested in floods for the exchange of transport–flood ICF. These findings are useful for stakeholders from all communities, transport planning institutions, and flood managers who are not fully benefiting from the extended use of ICTs to manage travel activities in floods.

This study aims to evaluate the environmental and economic performance of biodiesel production from mixed vegetable oil waste using the life cycle assessment (LCA) model. Due to its huge potential, Pakistan is taken as a case study. It produces 468,842 tons of vegetable oil waste annually. As no biodiesel production plant exists to process it, the environmental performance of biodiesel prototypes has not been investigated. Therefore, the current study is conducted to support the design of a plant to produce biodiesel from mixed oil waste. An attributional LCA was conducted using ReCiPe (H) and found that 400 kg of biodiesel can be produced from 1 t of mixed oil waste. The results, based on a functional unit of 1 ton, showed that biodiesel production from mixed vegetable oil waste is more eco-friendly than the existing landfilling practices with a global warming potential of 1.36 × 10⁻⁴ kg CO₂ eq, human toxicity of 5.31 kg 1.4 DB eq, ozone depletion potential of 0.00271 kg CFC-11 eq, eutrophication potential of 0.0118 kg P eq, acidification potential of 123 kg SO₂ eq, and photochemical ozone formation of 51.4 kg NO_x eq. Scenario modelling was conducted using electricity from photovoltaic solar cells, which decrease fine particulate matter formation from 44.5 to 0.725 kg PM₂.₅ eq, instead of using electricity from a grid to the plant. Hotspot identification was carried out to highlight the effects of individual impact categories. An economic analysis showed that 638,839 USD/year revenue would be generated. Generating energy from discarded vegetable oils through biodiesel production presents a sustainable and economically viable approach. This process benefits the environment and contributes to cost savings by reducing waste disposal in landfills. Furthermore, it aligns with the principles of a circular economy, in which resources are reused and recycled. It also supports the pursuit of the United Nations’ Sustainable Development Goals (SDGs), particularly SDG-7, which focuses on affordable and clean energy, and SDG-12, which emphasizes responsible consumption and production.

This paper describes a multi-feature dataset for training machine learning classifiers for detecting malicious Windows Portable Executable (PE) files. The dataset includes four feature sets from 18,551 binary samples belonging to five malware families including Spyware, Ransomware, Downloader, Backdoor and Generic Malware. The feature sets include the list of DLLs and their functions, values of different fields of PE Header and Sections. First, we explain the data collection and creation phase and then we explain how did we label the samples in it using VirusTotal's services. Finally, we explore the dataset to describe how this dataset can benefit the researchers for static malware analysis. The dataset is made public in the hope that it will help inspire machine learning research for malware detection.

This paper reports the undermined potential of broad range of (Information and communication technologies) ICTs that remained effective yet unnoticed in different flood-phases to exchange traffic, travel, and evacuation related information. The objective was to identify convenient ICTs that people found operational in life cycle of a flood. For the purpose, ICTs were tested in relation to 18 different variables based on personal capabilities, demographic, and vehicle-based information etc. Samples of 105 and 102 subjects were recruited from flood-prone communities of developing and developed case-studies respectively, through random sampling and analyzed through Multinomial Logistic Regression. Those categories of independent variables that showed p-value ≥ 0.05 were considered to model the results. The main findings showed that in developed countries TV, mobile phone subscriptions and international news channels were prominent source of information whilst in developing countries multiple messengers, Facebook and contributory websites were impactful for information dissemination. The results are useful for academia, engineers, and policy makers and for future work same variables can be tested for different disaster affected communities.