Transportation Choices Research Articles

Assessing the Impact of Public Behavior and Industrial Emissions on Ambient Air Quality in Pakistan

Air pollution is a growing environmental concern worldwide, and Pakistan is no exception. The study investigates the impact of industrial emissions, as well as public behavior, on air pollution in Pakistan. For this purpose, a questionnaire-based survey was conducted among a sample size of 100 participants, including students, professors, and government employees. The goal was to assess their attitudes, practices, and understanding regarding air pollution, as well as their perception and contribution to the relevant issues and their role concerning industries and vehicles. The findings revealed significant insights into the prevailing situation. The respondents recognized the contributions of industrial emissions to pollution levels. However, a substantial portion of the participants exhibited limited knowledge regarding specific pollutants and their sources. These responses clarified certain facets regarding public behavior, including transportation choices, waste management practices, and energy-efficient technologies. It was evident that sustainable modes of transportation, such as cycling or public transportation, were underutilized, with personal vehicles being the primary choice. Furthermore, significant challenges were identified as a lack of proper waste management practices and the low adoption of energy-efficient technologies. Based on these findings, it is crucial to implement targeted interventions and raise awareness to address air pollution in Pakistan effectively. The study highlighted that there is adire need for increased education and public outreach initiatives to improve knowledge regarding air pollutants and their sources. Policies that support environmentally friendly transportation choices and encourage industry adoption of cleaner technologies should also be developed.

Role of Cargo Owner in Logistic Chain Sustainability

Understanding the factors and characteristics of the business and the influence of cargo owners on sustainable practices in maritime logistics chains is the main objective of this study. The sample consists of 141 valid responses from Portuguese companies that own cargo, freight forwarders and other maritime logistics service providers. Sustainable energy theory, green state theory, and shared value creation theory support the research. The SEM methodology was adopted. The sector’s structure, management characteristics, type of transport contract and the size of the cargo characterize and condition the business of cargo owners and influence the choice of green transport, the use of green fuel and corporate social responsibility. It is important to increase the knowledge and practice of cargo owners in order to understand their business constraints.

Multiple-Criteria Decision-Making for Medical Rescue Operations during Mass Casualty Incidents

Mass casualty incident (MCI) is an unpredictable situation where a great number of people have been injured after an accident or sudden disease. Survival of the injured in the MCI depends on the efficiency of the directed emergency system (DES). The organization and management of medical assistance is of paramount importance. The shortest possible time to provide medical services to injured persons is crucial. The medical service in the case of the MCI primarily requires decisions on the priority of the order of treatment of the injured, the choice of medical transport and the location of specialized emergency treatment. As part of this paper, the effectiveness of the DES has been analyzed, and criteria used to improve rescue operations have been formulated. A formalized mathematical description of the medical rescue operations in MCIs has been proposed, and the optimization problem as the mixed integer linear programming (MILP) task was formulated. Optimization of an example case of rescue operations in MCIs has been presented. A computer simulator for optimal decision-making in medical rescue operations (CSMRO) has been developed for this purpose. The CSMRO implements various multi-criteria optimization methods to solve the formulated problem of rescue operations optimization. The results of computations made with the developed CSMRO simulator significantly shorten the time of decision-making in mass casualty incident handling.

How Sustainable Transportation Can Utilize Climate Change Technologies to Mitigate Climate Change

The build-up of greenhouse gases (GHGs) is causing warmness in the Earth’s atmosphere, resulting in climate change. The transport sector is one of the active causes of GHG emissions and it is imperative to use sustainable transport sources to control climate change. There is a measure that aims to encourage citizens to stop using their own vehicles as their choice of transport and instead opt for joint sustainable mobility during traveling. In this study, a quantitative research method was used and data were collected from a sample of 410 respondents through a questionnaire. Furthermore, this study also took a simulation-based (n = 10,000) sample size of electric rail vehicle data. The data were analyzed using structural equation modelling. The results revealed that sustainable transportation, climate change technologies, and electric rail vehicles reduce climate change in the ecoregions of China. We conclude that sustainable transportation policies could be formulated and implemented to reduce climate change. In response to the research results, it is recommended that, since climate change is a multi-level governance issue, the outdated pyramidal transport industry models must be shifted to a sustainable transportation system model.

Development of a Heritage-Themed-City in Jaipur for Sustainable Urban Development

The goal of the building of a heritage-themed city for sustainable urban development in Jaipur is to maintain the city's cultural history while incorporating sustainable techniques. The historical and cultural significance of Jaipur, often known as the Pink City, draws tourists from all over the world. This initiative stresses the preservation and restoration of historical structures, monuments, and cultural locations via the use of architectural conservation and maintenance procedures. For the city's long-term existence, sustainable infrastructure solutions, such as energy-efficient buildings, renewable energy sources, effective waste management, and water conservation measures, are essential. The incorporation of green areas, pedestrian-friendly zones, and non-motorized transportation choices promotes sustainability further. Essential components of the development plan include adaptive reuse of historic structures for modern uses, community engagement, responsible tourist management, education, cooperation, and monitoring. By adopting these concepts, the heritage-themed city in Jaipur may serve as an example for sustainable urban development, preserving its cultural legacy while providing a dynamic and living environment for inhabitants and tourists. Key Words: Sustainable urban development, Cultural heritage, Preservation, Restoration, Sustainable infrastructure

A Probabilistic Simulation Framework to Assess the Impacts of Ridesharing and Congestion Charging in New York City

Understanding the holistic city-wide impact of planned transportation solutions and interventions is critical for decision making, but challenged by the complexity of the urban systems, as well as the quality of the available urban data. The cornerstone for such impact assessments is estimating the transportation mode-shift resulting from the intervention. Although transportation planning has well-established models for the mode-choice assessment such as a nested multinomial logit model, an individual choice simulation could be better suited for addressing the mode-shift allowing us to consistently account for individual preferences. Moreover, the available ground-truth data on the actual transportation choices is often incomplete or inconsistent. The present paper addresses those challenges by offering an individual mode-choice and mode-shift simulation model and the Bayesian inference framework, and demonstrates how impact assessments can be performed in the events of incomplete mobility data. It accounts for uncertainties in the data as well as the model estimate and translates them into uncertainties of the resulting mode-shift and the impacts. The framework is evaluated on the two intervention cases: introducing ride-sharing for-hire-vehicles in NYC as well the recent introduction of the Manhattan Congestion surcharge. It can be used to assess mode-shift and quantify the resulting economic, social and environmental implications for any urban transportation solutions and policies considered by decision-makers or transportation companies.

Shouting “chin min yau lok” (stop at the front) in a minibus: Transportation assimilation among immigrants in Hong Kong

Transportation socialization as a rising field of study has gained much attention in traditional immigration countries, such as the United States and European countries. Treating transportation behaviors as a routine activity, previous studies mainly looked at the discrepancy in transportation choices between immigrants and natives, for example, automobile usage in the U.S. By examining immigrants’ minibus ridership in Hong Kong (a unique local public transportation service) and extending the previous theoretical thread on spatial assimilation, this study expands the social and geographical scope of transportation assimilation to a non-traditional immigration region and further tests the applicability of general immigration theory on transportation socialization. Capitalizing on a large sample of pooled census data, we are the first to explore the changing pattern of immigrants’ transportation socialization in Hong Kong and Asia. By looking at the transportation assimilation through individual and locational level factors, we found the pattern in Hong Kong to be generally in line with the previous studies in traditional immigration countries on the individual level but not on the locational level.

Associations between biopsychosocial factors and transportation patterns of older adults residing in Klang Valley, Malaysia.

Aging is associated with physiological changes in multiple systems in the body and may impact the transportation choices of older adults. In this study, we examine the associations between biopsychosocial factors and the transportation choices of Malaysian older adults. One hundred and nineteen (119) older adults, aged 60 and above, living in Klang Valley, Malaysia were recruited for this cross-sectional study. Participants were interviewed face-to-face to obtain sociodemographic data, health status (whether there were and, if yes, the number of comorbidities), outdoor mobility and transportation patterns, Instrumental Activity Daily Living (IADL) status and cognitive function. Participants' physical performance (dominant handgrip strength, 10-m walk, and timed up and go tests), hearing threshold (pure tone audiometry), and vision function (visual acuity, contrast sensitivity) were measured. Transportation patterns of older adults were categorized into three groups, that is, flexible (using public transport and/or private vehicles), using only private vehicles and restricted (relying on others or walking). Further information is needed to enable such older adults as older women, those with comorbidities and poorer functional status to access transportation, especially to meet their health care needs. The majority (51%) of participants were in the 'using only private vehicles' group, followed by the 'flexibles' (25%) and the 'restricted' (24%). Factors significantly associated with the restricted transportation group were: (a) being female (AdjOR 15.39, 95% CI 0.86-23.39, p < 0.001); (b) being Malay (AdjOR 21.72, 95% CI 0.36-16.12, p < 0.001); (c) having higher number of comorbidities (AdjOR 14.01, 95% CI 0.20-13.21, p = 0.007); and (d) being dependent in IADL (AdjOR 13.48, 95% CI 0.51-1.78, p = 0.002).

Micro-macro multilevel analysis of day-to-day lifestyle and carbon emissions in UK multiple occupancy households

Far-reaching changes in daily life present a pressing need to balance energy consumption with environmental impact. Previous research on household carbon emissions generally described its contributors in disposable income, consumption pattern, and household-related lifestyle, whereas they have not fully explored how carbon emissions relate to residents' day-to-day lifestyles. Given that individual lifestyles within a household may be correlated, there is a need to disentangle the clustering effect of household members' lifestyles and their association with household carbon emissions. This study used micro-macro multilevel modelling to examine the structure of individual lifestyles and their impact on household carbon emissions for 8618 multiple occupancy households of 19,816 respondents in the UK Household Longitudinal Study dataset. The results showed that a factor capturing energy-saving lifestyle behaviours significantly reduced housing fuel use emissions and a second capturing transportation and consumption choices cut motor emissions. Interestingly, the contribution of energy-saving lifestyle in cutting down housing-fuel-using emissions becomes more pronounced when household income and household characteristics (e.g., household size, dwelling, house ownership, number of cars, urbanity, employment) were controlled for. Contrarily, the strength of green transportation and consumption lifestyle contributing to lower motor emissions was weakened after controlling for household characteristics. Findings indicated that day-to-day lifestyle not only reflects individual variability in sustainable living but also systematic household variation in carbon emissions. Knowledge of which living patterns are responsible for disproportionately high levels of carbon emissions can enhance effective targeted policy aimed at stimulating sustainable lifestyles and carbon reduction.

The preferences of transport mode of certain travelers in the age of autonomous vehicle

Individuals choose transport modes to travel based on their preferences and journey characteristics. The availability of autonomous vehicles (AVs) on the market will affect the traditional mode choice models. In this regard, this research studies three transport modes: conventional car, privately-owned autonomous vehicle (PAV), and shared autonomous vehicle (SAV). The potential changes in transport mode choice are evaluated, where the impact of changes of some sociodemographic and travel variables on choices are estimated. A discrete choice modeling approach is applied in this study to develop a transport choice model. A stated preference (SP) approach is used, where 306 answers are collected in Hungary. A discrete choice experiment (DCE) is designed to collect the choices of people. Multinomial Logit (MNL) model is applied to the data to develop a transport mode choice model, where differentiations between some parameters across transport modes are presented. People are varied in their willingness to use a transport mode across groups, such as the income, the family size, and the current transport mode. The results demonstrate that people are more likely to use a conventional car than PAV, while they are more likely to use PAV than SAV. The output of this study can be used to support policy implications in the AV era.

PERAMALAN JUMLAH PENUMPANG KERETA API DI PULAU JAWA MENGGUNAKAN METODE HOLT WINTERS EXPONENTIAL SMOOTHING DAN FUZZY TIME SERIES MARKOV CHAIN

One of the public transportation choices by the public is the train. The number of train passengers on the island of Java often increases and decreases in certain months. PT.KAI can monitor the number of train passengers by forecasting. Forecasting aims to predict the number of train passengers so that PT.KAI is ready to provide the best service. This study uses monthly data on the number of train passengers on Java Island from January 2015 to February 2020. This study uses multiplicative holt winters exponential smoothing and fuzzy time series markov chain. The multiplicative Holt Winters exponential smoothing method is used on data that contains trend and seasonal elements that experience data fluctuations simultaneously. The fuzzy time series markov chain method is a combination of the fuzzy time series with the markov chain which aims to obtain the greatest probability using the transition probability matrix. Based on the analysis results, it can be concluded that the multiplicative holt winters exponential smoothing method is better at predicting the number of train passengers on Java Island because the value of sMAPE multiplicative holt winters exponential smoothing is smaller, it is 3,0643% and the sMAPE fuzzy time series markov chain value is 5,2955%.

GL-Net: Semantic segmentation for point clouds of shield tunnel via global feature learning and local feature discriminative aggregation

has gradually become the first choice of modern urban public transportation due to its advantages of safety and high-efficiency. Shield tunnel is an important type of subway tunnel, and its structural stability and safety play an important role in subway operation. The shield tunnels are prone to problems such as water leakage and tunnel collapse, which affect the safe operation of subways. Efficient monitoring methods are required to detect the status of subway tunnels. The data collection and accurate segmentation of key components of shield tunnels are the basis and key to the automatic monitoring of subway tunnels. This research presents a novel semantic segmentation method of three-dimensional (3-D) point clouds of typical structural elements (e.g., longitudinal joint, circumferential joints, bolt hole and grouting hole) in shield tunnel based on deep learning. In this method, we focus on how to make the network learn robust global features and complex local distribution patterns. Further, we propose a global and local feature encoding block (namely GL-block) to discriminatively aggregate local features while learning global representation. After multiple encodings by the GL-block, we design a global correlation modeling (GCM) module to establish a global awareness of each point. Finally, a weighted cross-entropy loss function is designed to solve the problem of unbalanced number of samples in each category of shield tunnel. In the experiments, we make a dataset of shield tunnel point clouds with a length of about 1,000 m collected by CNU-TS-1 (DU et al., 2018) mobile tunnel monitoring system, and use the dataset to train and test the segmentation ability of our method on the typical structural elements of shield tunnels. Experiments verify the effectiveness of our method by comparing with the other state-of-the-art 3-D point cloud semantic segmentation methods, and our method has an mIoU score of 73.02 %, which is at least 14.54 % higher than the other compared state-of-the-art networks. Also, we further verify the adaptability of our method to different tunnels and different laser scanning equipment, such as FARO, Leica and Z + F, and achieve very advanced performance.

METHODOLOGY FOR CALCULATING THE PERFORMANCE AND FUNCTIONALITY OF QUARRY EXCAVATORS

Conducted review of methods of calculation of productivity of open-pit excavators and results of verification calculations shows that, there is no uniform method that counts constantly changing physical and mechanical properties of developed rocks, technological parameters of a face, design and force parameters of an excavator, probability of its no-failure operation, casual character of transport delivery, qualification of the excavator operator, climatic conditions. Calculation of productivity of open pit excavators under specific mining and geological conditions is made for planning and rationing of excavation and loading machines, the choice and provision of transport, and establishment of a rational mode of their operation. In the given conditions of open-cut mining, an excavator’s productivity depends on many natural, mining-technological, and organizational factors, which are difficult to take into account and largely interact with the design parameters of open-pit excavators. In fact, most of the factors are probabilistic in nature. As a consequence, it is advisable to define values of productivity for each individual case based on the cumulative account of changes in multifactorial conditions of operation and technical condition of technologically connected equipment. In this regard, it is of interest to determine the value of productivity of quarry excavators with the account of the stated factors.

“Another (hard) day moving in the city”: Development and validation of the MCSS, a multimodal commuting stress scale

Although most of the literature on stress-related factors uniformly points out several negative health, performance, and safety issues as potential outcomes attributable to a prolonged exposition to stressful situations, the applications of these assumptions to the field of urban mobility (and more specifically to commuting) remain considerably scarce. Indeed, while commuters worldwide usually face daily situations such as traffic jams, dearth of transport choices, crowded public transport services, long trips, and road conflicts, there are only a few studies that have methodologically addressed the issue of commuting stress (CS). AimThis paper aims to present in detail the development and validation of the Multimodal Commuting Stress Scale (MCSS), a self-report tool for assessing commuting stress-related factors among urban commuters of different modalities. MethodsThis cross-sectional study retrieved data from a nationwide sample of 1,429 commuters (aged between 18 and 78; average 34.42 years) from all 17 Spanish regions. Data were analyzed through Structural Equation Model-based competitive Confirmatory Factor Analyses, thus testing the psychometric performance of the scale under different structural assumptions, as well as its reliability, consistency, and validity insights. ResultsThe outcomes of this study suggest that the developed MCSS has a clear four-dimensional structure, an optimal set of factor loadings, fair goodness-of-fit indexes, and satisfactory internal consistency and reliability. Additionally, validity analyses show that commuting stress factors measured through the MCSS have coherent associations with both physical/mental health indexes and trip-related factors, while it also discriminates commuting stress scores by sex, age, and commuting modality. ConclusionAll in sum, the findings of this study endorse the suitability of the Multimodal Commuting Stress Scale for measuring stress-related factors affecting urban commuters, as well as to make comparisons among different groups of them.

A Decision Support Model for Cost-Effective Choice of Temperature-Controlled Transport of Fresh Food

The application of a plethora of wireless technologies to support real-time food quality monitoring during transportation has significantly improved the performance of fresh food delivery systems. However, deployment of these technologies increases the capital and operational costs of food delivery and, hence, not all food delivery operations need to employ them. This paper looks at the trade-off of the costs involved in utilizing these technologies with the nature of food delivered, the length of transportation, and the perceived costs of food wasted using a linear programming model. The problem is formulated over a bi-echelon network with the possibility of transporting the fresh produce through dry vans, vans with temperature control but without monitoring capability, and vans with temperature control and monitoring capability. Results indicate that under situations of infinite vehicle resource availability, the optimal choice of the van type is independent of the demand levels; however, the optimal choice changes for different travel distances and the value of penalty costs (of allowing food to go waste). For example, technologies that maintain and monitor the temperature of storage conditions will be useful for food items that quickly become waste, especially when transported over longer distances and when the penalty costs are higher.

A data directory to facilitate investigations on worldwide wildlife trafficking

ABSTRACT Wildlife trafficking is a global phenomenon posing many negative impacts on socio-environmental systems. Scientific exploration of wildlife trafficking trends and the impact of interventions is significantly encumbered by a suite of data reuse challenges. We describe a novel, open-access data directory on wildlife trafficking and a corresponding visualization tool that can be used to identify data for multiple purposes, such as exploring wildlife trafficking hotspots and convergence points with other crime, discovering key drivers or deterrents of wildlife trafficking, and uncovering structural patterns. Keyword searches, expert elicitation, and peer-reviewed publications were used to search for extant sources used by industry and non-profit organizations, as well as those leveraged to publish academic research articles. The open-access data directory is designed to be a living document and searchable according to multiple measures. The directory can be instrumental in the data-driven analysis of unsustainable illegal wildlife trade, supply chain structure via link prediction models, the value of demand and supply reduction initiatives via multi-item knapsack problems, or trafficking behavior and transportation choices via network interdiction problems.

Understanding Influencing Factors of Travel Mode Choice in Urban-Suburban Travel: A Case Study in Shanghai

After the rapid expansion of the subway system over the past two decades, some cities are preparing to build more suburban railways. The emergence of suburban railways is bound to change the choice of suburban passenger transportation. This paper studies the factors that affect the choice of travel mode at the construction stage of suburban railways, aiming to design a more rational suburban railway network and urban public transport service system. Taking Shanghai as an example, this study first surveyed revealed preference (RP) and stated preference (SP) among urban-suburban travelers. Then, we used discrete choice models (DCM) and machine learning algorithms to build a travel mode choice model based on data collection and analysis. Furthermore, the importance of each factor was analyzed, and the effects were predicted under several traffic demand management schemes. Finally, this study proposed some strategies for increasing the share of public transport. On the one hand, it is suggested that Shanghai should continue to develop suburban railways and maintain low pricing of public transport services. Considering the construction and operation costs, the government needs to provide certain subsidies to stabilize prices. On the other hand, as passengers are very sensitive to the “last mile” trips in their suburban railway travel, transport planners should strengthen the connection from and to the suburban railway stations by developing services such as shared bikes and shuttle buses. In addition, the results indicated that some traffic demand management measures can also contribute to a larger share of public transport.

Modeling of intra-city transport choice behaviour in Budapest, Hungary

The discrete choice modeling and decisions-tree technique are used to understand the travel behavior of people in Budapest. The discrete choice modeling is applied to develop transport mode choice that mimics the travel behavior of people using their personal and travel characteristics. A revealed preference (RP) survey was conducted by the Hungarian Census Bureau which contains information about the households in Budapest in 2014, is used. Understanding the daily main trips of people is firstly analyzed using decisions-tree technique, where the impact of each variable is presented based on its importance in affecting the travel choice mode. In random utility theory, travelers choose one option out of certain available options to them to maximize their utility. The Multinomial Logit (MNL) model is used to examine the relationship between various variables connected to travelers in order to understand the travel behavior pattern. The result of the analysis shows a clear pattern between car ownership and each of family size, and age, and trip cost variables. The result of decision tree analysis demonstrates that travelers’ trip duration is the most important factor that has impact on their transport mode choice, which is mainly distributed within private cars, public transportation, and walking. The developed multinomial transport mode choice model includes sociodemographic, economic, and travel characteristics. The trip time, trip cost, age, car ownership index, trip purpose, gender, employment, and income are the main determinants the impact the transport choice mode. The developed models (i.e., decision tree and the multinomial transport logit choice) are beneficial for the decision-makers who can used it in predicting the travel demand.

Ways to go? (un)sustainable school commuting in Majorna, Gothenburg city

ABSTRACTGothenburg city has bold ambitions of becoming carbon neutral. School commuting is one piece of the puzzle in reducing emissions. While the literature on school transportation is extensive, the issue of climate change has been overlooked. This article explores how parents in the district of Majorna understand mundane choices of school transportation in a context of increasing recognition of climate change. The article shows that school transport is a contentious issue, entangled with subjectivity, emotions, and notions of responsibility. The findings also highlight some complexities: (1) Although most parents are concerned with climate change it is not a significant factor in daily transportation. (2) There is a discourse in favour of active transportation where climate change is explicitly downplayed, on the other hand regular car use merges with deep climate concerns. (3) Informants’ anticipations of future urban traffic conflict with their hopes, yet it seems difficult to imagine something otherwise.

For Everyday Life

For Everyday Life Ndaba Sibanda (bio) Perhaps the expression "life is what you make it" is still relevant today in spite of some people`s personal beliefs, ones that are rooted in the power of fate or destiny rather than in decision or action. After working for several months or years, sometimes we feel the need to go on vacation to recharge our batteries. One day I made a choice to visit the mighty Victoria Falls in Zimbabwe. I also chose the mode of transportation I would use to get to that world-famous tourist center, the Mosi-oa-Tunya ("the smoke that thunders"), also known as the Adventure Capital of Africa and the Adrenalin Capital of Africa. The linking buzzword between my choice of destination and my choice of transportation was none other than mathematics. I am a teacher by profession and a poet by avocation. As an educator and author, I use my laptop on an almost daily basis for classroom lessons, charts and graphs, writing, and data storage. (I consider any working computer a mathematical genius.) Teaching has been known to be a noble profession. Like any job, what makes it noble is how one applies mathematics to one's personal and professional schedules. Without that mathematical flair, it can be an ignoble and thankless occupation. Jamie Lyn Beatty Thi is of the opinion that, "Jobs fill your pocket, but adventures fill your soul." (Twitter, 22 December 2016) Traveling begets memories. No amount of money can buy our recollections of our experiences. We are the best witnesses of our travels and experiences. A person who hits a jackpot and then goes on a spending spree without accounting for each and every cent is like a businessperson who cannot work out how much they need to charge for their services or goods. Evidently, the danger of failing to think logically or mathematically can lead into a disaster no matter how much money one has. Along Victoria Falls Road, I saw few thriving businesses and farms. I knew that the owners had basic models that detailed their business concepts like procedures, processes, practices, production costs, staff wages, assets, and liabilities. Some shop attendants were doing stocktaking and bookkeeping. Mathematics was certainly at work. Of course, I do not need to be a rocket scientist or a mathematician to know that the salary and perks of a job need to be enough to keep the [End Page 17] family ship afloat. If it is not enough, mathematics tells me that there is sufficient reason to jump ship jobwise. That means that the first port of call before one signs a contract is to examine the overall value of the job. For example, I would do a calculation of my expenses. How much money would go into fundamental bills like rent, utilities, car insurance and maintenance, house maintenance, fuel consumption, school fees, and life and health insurance? How much money would I save after paying those bills? Would I have enough for capital projects like extending a residential property or investing in a business venture like a café? Would I have enough money to spoil myself and my family once in a while with outings and gifts, or enough to keep up with technological advances in household gadgets, laptops, and mobile phones? I remember that before I embarked on the trip to Victoria Falls, I had to consider time and the duration. Would it be a two-day adventure? Would I travel by car or by train or by plane? Being a big fan of safaris, I decided to go between May and October, when the vegetation is comparatively low and animals are easily spotted. Of course, this happened after determining several figures: the temperature, the best time to visit Victoria Falls, and the specific experiences I sought to invest myself and time in. Being fearful of a night safari, I chose a morning drive. I told myself that next time I would go in the afternoon. Some people chose to rent bikes or cars, but I said, "Canoe and copter, take me away." Next time, maybe a horseback expedition. Why not? Victoria Falls is not only a World Heritage Site...