Explanation of factors influencing cyclists’ route choice using actual route data from cyclists

Cycling as a sustainable means of transport brings a number of benefits, which includes improved health and well-being for individuals, improved air quality and climate change, accessibility and reduced traffic congestion at the national level. However, despite the benefits of cycling and the efforts by the government to promote this mode of transport, many short trips in Britain suitable for cycling are still made by motorised transport modes. People seem reluctant to change their mode of travel behaviour in favour of cycling. Therefore, it is important to understand the nature of complicated behaviour of people and the ones of cyclists at first. The thesis aimed to understand route choice behaviour for cycling for utility purposes in England. The thesis examined why cyclists use their current routes and how various features influence their choices. The thesis also probed the reasons for the choices and the relationship between the choice and the characteristics of cyclists. A mixed method approach was applied for the thesis, using questionnaires, actual route data collection for quantitative methods and interviews for qualitative methods. This approach allowed the researcher to examine diverse aspects of the research questions, which individual methods were unlikely to address. The thesis has identified what route features are important for cyclists, and why these features are considered important. In terms of the issues regarding cycling infrastructures, the preferences of cyclists were found to be linked to the fear to motorised traffic on roads, which is a fundamental issue that may not be revealed through quantitative studies. Another key finding identified was that cyclists choose different routes dependent on the conditions applicable even for same trip purposes. In this respect, it was noted that often their choices are forced by prevailing road instructions such as one-way road, although they may be aware that the alternative road conditions may not be good from a cycling viewpoint. However, it was also found that, where practicable, cyclists are likely to choose a route strategically, in a manner that will minimise the physical efforts required for cycling. Finally, based on the observations of the different geographical and environmental characteristics and atmosphere to cycling in two case study cities, the thesis also discovered the segment of the population who could become the main target for promoting the benefits of cycling

Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method

It is common to call a taxi by taxi-apps in Korea and it was believed that an app-taxi service would provide customers with more convenience. However, customers’ requests can often be denied, as taxi drivers can decide whether to take calls from customers or not. Therefore, studies on factors that determine whether taxi drivers refuse or accept calls from customers are needed. This study investigated why taxi drivers might refuse calls from customers and factors that influence the success of matching within the service. This study used origin-destination data in Seoul and Daejeon obtained from T-map Taxis, which was analyzed via a decision tree using machine learning. Cross-validation was also performed. Results showed that distance, socio-economic features, and land uses affected matching success rate. Furthermore, distance was the most important factor in both Seoul and Daejeon. The matching success rate in Seoul was lowest for trips shorter than the average at midnight. In Daejeon, the rate was lowest when the calls were made for trips either shorter or longer than the average distance. This study showed that the matching success for ride-hailing services can be differentiated particularly by the distance of the requested trip depending on the size of the city