Abstract
The development of efficient mass transit systems that provide quality of service is a major challenge for modern societies. To meet this challenge, it is essential to understand user demand. This article proposes using new time-dependent attributes to represent demand, attributes that differ from those that have traditionally been used in the design and planning of this type of transit system. Data mining was used to obtain these new attributes; they were created using clustering techniques, and their quality evaluated with the Shannon entropy function and with neural networks. The methodology was implemented on an intercity public transport company and the results demonstrate that the attributes obtained offer a more precise understanding of demand and enable predictions to be made with acceptable precision.
Highlights
According to the International Energy Agency, there were an estimated 870 million passenger light-duty vehicles on our roads worldwide in 2011, a figure that is projected to grow to 1.7 million in2035 [1]
The World Health Organisation estimates that approximately 3 million people die every year due to health problems caused by pollution [2] and in the European Union 250,000 people are victims of traffic accidents, and about
Cluster 2, which is associated with an average demand profile, is concentrated in the months of July and August, a holiday period in which travel to school decreases
Summary
According to the International Energy Agency, there were an estimated 870 million passenger light-duty vehicles on our roads worldwide in 2011, a figure that is projected to grow to 1.7 million in2035 [1]. This type of mobility, based on private vehicles, is resulting in the deterioration of health, the environment and safety on the roads To illustrate this problem, the World Health Organisation estimates that approximately 3 million people die every year due to health problems caused by pollution [2] and in the European Union 250,000 people are victims of traffic accidents, and about. Large-scale public road transport systems are an effective means to respond to mobility needs in a way that is safer and more respectful towards our health and the environment For this reason, the development of efficient transportation systems that provide quality of service is a priority for the authorities and for transport agencies. Models and techniques that contribute to the development of efficient systems and that provide quality of service are a topic of great interest for the academic community
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