Public Transport Accessibility: A Literature Review

Accessibility is one of the most important outcomes of the transportation system. Public transport can be more attractive by providing "Door to door mobility" and development of transportation services is an important factor of social quality. Public transport accessibility has gained vital importance in designing and evaluating the transit system in terms of mobility and sustainability. Apart from the transport system itself, public transport accessibility has the considerable impact on life satisfaction in the form of perceived accessibility. Moreover, researchers have revealed several impact and correlation of provision of public transport accessibility to the environment and daily life which would have a noticeable impact on public health and other aspects of public daily life. Furthermore, the correlation between public transport accessibility and job opportunities has attracted the researchers' attention in the literature. Also, public participation in social activities has been investigated based on public transport accessibility and their close connection has been revealed under the topic of social exclusion. In this paper, the available literature on public transport accessibility has been reviewed. As a conclusion, it should be highlighted that not just the performance of public transportation but its impact on other social aspects should be considered while planning the public transport facilities

A data-driven decision support tool for public transport service analysis and provision

Public transport service (PTS) analysis and provision is an important and challenging issue for public transport agencies. The results of the PTS analysis help transport planners to identify the areas in need of PTS improvement. Furthermore, relevant policy actions need to be determined for service provision to reach the desired level of PTS improvement in the identified areas. Without an appropriate decision support tool, planners need to apply several blind trials to find a policy action which improves the PTS in the examined areas. This paper introduces a data-driven decision support tool for PTS analysis and provision. The proposed framework combines a potentially large number of PTS measures while taking the correlation among the investigated measures into account and develops high-dimensional supervised classification models that predict the PTS levels for different policy actions. With this approach, planners can identify and prioritize the areas in need of PTS improvement, determine what policy actions should be targeted to improve the PTS in the identified areas, and predict the PTS impacts of these policy actions in the examined areas. The application of the proposed framework is demonstrated in detail through a case study of Budapest, Hungary, which is followed by a hypothetical policy implementation. The results show that mostly outskirts are in need of PTS improvement. Furthermore, the underlying reasons behind the areas with poor overall PTS are studied to target the relevant policy actions that improve the PTS in the identified areas. The PTS impacts of the targeted policy actions are studied by using the developed high-dimensional supervised classification models

Exploring Shared-Bike Travel Patterns Using Big Data: Evidence in Chicago and Budapest

Bike-sharing systems are an emerging form of sharing-mobility in manycities worldwide. The travel patterns of users that take advantage of smart devices to ride a shared-bicycle in two large cities (Chicago and Budapest) have been investigated, with analysis of approximately two million transaction data records associated with bike trips made over a three-month period in each location. Several aspects of user travel behavior—such as day and time of travel, frequency of usage, duration of usage, seasonal and peak/off-peak variations, major origin/destinations—have been included in this analysis. The results show that in both cities the bike-sharing option is a male-dominated alternative, particularly welcomed by younger groups, with the largest share of trips occurring in the afternoon peak. Appropriate usage of opensource big-data provides important lessons for successful vehicle sharing models,allowing the application of the findings to other cities and mobility options wherethese systems are still developing