Globally, transportation systems are defined by different factors. One of the most well known factors is the purpose of the transportation. For example, there may be public transportation, air, freight transportation. Therefore, it is expected that there would be differences in each of the systems based on the same, or similar, factors. The purpose of this report is to discuss the factors that impact transportation systems in different regions, based on environment, geography, infrastructure, and sustainability, and technology influences.
Per Demir, Bektaş, and Laporte (2014), carbon dioxide emissions have increased significantly as a result of road freight transportation. Therefore, the reduction of carbon dioxide emissions through effective route planning requires that developers understand emission models and how they influence optimization methods. At the same time, it is recognized that road freight transportation systems are essential for economic development. In developing countries, for example, part of the barrier to sound economic development is lack of effective transportation (Demir et al., 2014). Until the early 2000s, most freight transportation activity planning has centered around cost minimization. More recent, there have been global concerns regarding the negative impact of transportation methods, such as climate change influences, pollution, resource consumption, land use deterioration, accidents, and noise. In fact, locally and regionally, most freight transportation occurs by trucks, which lead to significant amount of pollutant emission, despite the advancement in fuel and transportation technology. The use of diesel engines, commonly used in trucks, contribute significantly to nitrogen oxide emission (Demir et al., 2014). Therefore, environmental influencers impact how transportation systems are developed, both in terms of mechanics (the vehicle used) and the route used for transport purposes.

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Geography has a major impact on transportation systems. Not only does geography consider the physical landscape, but also population density. Megacities, for example, are increasingly concerned about urban transportation impacts. It has been argued that rapid urbanization has directly impacted sustainable development, prompting different public and private sector actors to become involved in urban transportation system design and operations. These actors are typically focusing on optimizing the objectives of that particular sector (Camargo Pérez, Carrillo, & Montoya-Torres, 2015). It is recognized that urban passenger transportation systems are very complex and involve multiple criteria based on socio-political, environmental, and economic issues. Therefore, multi-criteria decision-making techniques are beneficial in assessing problems based on these criteria. The geography of the land is included in these criteria, as are community needs (Camargo Pérez et al., 2015).

Modern society is dependent upon the transportation system. However, transportation systems notoriously are associated with resilience and vulnerability. Transportation system vulnerability literature has increased in recent years, allowing for the identity of two traditions. The first tradition involves the study of transportation system vulnerability based on topological properties and is based on graph theory. The second tradition involves assessing consequences of disruptions and/or disasters for transportation users and society based on demand and supply theory (Mattsson & Jenelius, 2015). The resilience concept emphasizes a socio-technical perspective of the capacity of the transportation system to maintain or recover functioning after disruption and/or disaster, and is studied at a lower rate, especially in relation to post-disaster recovery. This means that the study of vulnerability and resilience is critical to understanding infrastructure influences on the transportation system and requires collaborations across multiple disciplines in order to be studied effectively, as well as to improve existing infrastructure-related issues that impact the transportation system.

Per Ribeiro, Carvalho, and Telhada (2015) recognized that conventional public passenger transportation systems in rural areas are very inefficient and ineffective. These problems have arisen as a result of low population density, as well as high temporal and spatial dispersion. However, sustainability of transportation systems in rural areas have been achieved through the use of Demand Responsive Transport systems, which have been adopted in several countries. The use of this type of system has been adopted in order to increase mobility through the provision of flexible transportation to meet individual requests. Significantly, through the use of the flexible transportation system, social exclusion relating to transportation issues has been mitigated (Ribeiro et al., 2015). Despite these benefits, some Demand Responsive Transport systems have been shown to be unsustainable or inadequate because of the dependency on organizational and functional parameter tuning, such as stop locations, schedule flexibility, and routes. There is much literature regarding Direct Responsive Transport systems, yet few have tackled problems relating to the inefficiencies of the system. As a result of the decreased efficiency, Big Data may be used to analyze the parameters discussed, per Wu and Chen (2017), which may lead not only to transportation system sustainability, but also environmental sustainability. Ribeiro, Carvalho, and Telhada (2015) sought to use a new integrated multi-disciplinary decision support system to effectively design, develop, plan, and establish Direct Responsive Transport systems, which can lead to improved ability for sustainability assessment.

Through these four major categories – environment, geography, infrastructure, and sustainability – it is seen how different regions can have different viewpoints regarding their transportation systems. A significant cause of this difference is related to the goals of the individual regions. For example, Mexico may not have the exact same goals as Japan, which may influence the type, extent, and even the actual implementation decision of transportation systems. As a result, it is evident that many different criteria must be taken into consideration when deciding to establish transportation systems and to base these systems upon the most pressing needs of the region.

  • Camargo Pérez, J., Carrillo, M. H., & Montoya-Torres, J. R. (2015). Multi-criteria approaches for urban passenger transport systems: a literature review. Annals of Operations Research, 226(1), 69–87.
  • Demir, E., Bektaş, T., & Laporte, G. (2014). A review of recent research on green road freight transportation. European Journal of Operational Research, 237(3), 775–793.
  • Mattsson, L.-G., & Jenelius, E. (2015). Vulnerability and resilience of transport systems – A discussion of recent research. Transportation Research Part A: Policy and Practice, 81, 16–34.
  • Ribeiro, A. C., Carvalho, M. S., & Telhada, J. (2015). An Integrated Decision Support System to Assess the Sustainability of Demand Responsive Transport Systems. In Operations Research and Big Data (pp. 185–193). Springer, Cham.
  • Wu, P.-J., & Chen, Y.-C. (2017). Big data analytics for transport systems to achieve environmental sustainability. In 2017 International Conference on Applied System Innovation (ICASI) (pp. 264–267). IEEE.