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The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George’s transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis. [ABSTRACT FROM AUTHOR]

To date there has been very little work on the machine understanding of imprecise diagrams, such as diagrams drawn by students in response to assessment questions. Imprecise diagrams exhibit faults such as missing, extraneous and incorrectly formed elements. The semantics of imprecise diagrams are difficult to determine. While there have been successful attempts at assessing text (essays) automatically, little success with diagrams has been reported. In this paper, we explain an approach to the automatic interpretation of graph-based diagrams based on a five-stage framework. The paper describes our approach to automatically grading graph-based diagrams and reports on some experiments into the automatic grading of student diagrams. The diagrams were produced under examination conditions and the output of the automatic marker was compared with the original human marks across a large number of diagrams. The experiments show good agreement between the performance of the automatic marker and the human markers. The paper also describes how the automatic marking algorithm has been incorporated into a variety of software teaching and learning tools. One tool supports the human grading of entity-relationship diagrams (ERDs). Another tool is for student use during the revision of ERDs. This tool automatically marks student answers in real-time and provides dynamically created feedback to help guide the student's progress. [ABSTRACT FROM AUTHOR]