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One of the major problems affecting global transportation system is timely arrival of travelers at their destinations. Transportation, whether public or private, plays an important role in peoples’ lives by helping them to move from one place to another, providing them an easy means of transporting their goods and services for optimum business transactions. There are different means of transportation; some people choose to travel by air, water or by land/road.
Road transportation involves the movement of people and goods from one point to another on roads. On roads, motor vehicles and animals are used to move humans, goods and services. A road is a land route that traverses between two destinations, constructed to enable transportation by way of motorized and non-motorized carriages (Nanzip, 2020). People engage in transportation for various reasons such as business, tourism or socialization. In most cases, many people travel via a given route at the same time of the day resulting in congestion on such routes. Consequently, commuters often get disgruntled with time wastage on the road, stress, and untimely arrival at their destinations.
The quality of a road transport system can be improved upon with the help of modern technology and intelligent transportation systems (ITS), resulting in more reliable and convenient road transport services. Accordingly, collaborative filtering recommender systems (CFRSs) can be suitable for optimum road transport services (Alexander, et al., 2017). Recommender systems are algorithms designed to make suggestions about items that are relevant to users. Such systems are designed to help users filter out irrelevant targets and recommend targets that may be of help to them. A recommender system helps a user to make choices following recommendations by the system when there is no sufficient personal knowledge or experience of the available alternatives (Isinkaye, Folajimi & Ojokoh, 2015).
Recommender systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at supporting users in various decision-making processes. Technically, recommender systems have their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). They have proven to be worthy tools for online users to deal with information overload and have become one of the most popular and powerful tools in E-commerce (Mehrbakhsh et al., 2013).
Recommender Systems (RSs) are used in a variety of areas, with commonly recognized examples taking the form of playlist generators for various services desired by users. These systems can operate using a single input or multiple inputs within and across platforms. Since recommendations are usually personalized, different users or user groups receive diverse suggestions. In addition, there are also non-personalized recommendations (Jannach, 2016). Recommender systems development is initiated from quite a simple observation: individuals often rely on recommendations provided by others in making routine, daily decisions (McSherry & Mironov, 2019). In simple form, RSs try to predict what is most suitable based on the user’s preferences and constraints. In completing such a computational task, RSs collect from users their preferences, which are either explicitly expressed, (e.g. ratings for path), or are inferred by interpreting user actions. In the case of road transportation, path recommender systems assist road users to consider a path to take before traversing such path.
A path or route recommender system can be used to identify multiple optional paths available in a given location, influenced by user rating or preferences using a subclass of information filtering system that seeks to predict the alternative desired route to the destination. The Collaborative Filtering Recommender System (CFRS) can provide such guidance on what path or route to choose among the vast available options that can lead to timely arrival (via rating or preference). The system can be applied for the provision of dependable traffic information to help road users including pedestrians to make better route choice decisions.