Name
Abstract | ||
|---|---|---|
An analysis of the characteristics and behavior of individual bus stops can reveal clusters of similar stops, which can be of use in making routing and scheduling decisions, as well as determining what facilities to provide at each stop. This paper provides an exploratory analysis, including several possible clustering results, of a dataset provided by the Regional Transit Service of Rochester, NY. The dataset describes ridership on public buses, recording the time, location, and number of entering and exiting passengers each time a bus stops. A description of the overall behavior of bus ridership is followed by a stop-level analysis. We compare multiple measures of stop similarity, based on location, route information, and ridership volume over time. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | public transportation, statistics, time series analysis, clustering methods |
Field | DocType | Citations |
Data mining,Simulation,Computer science,Scheduling (computing),Volume measurement,Operations research,Public transport,Schedule,Cluster analysis | Conference | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laura Tupper | 1 | 0 | 0.34 |
| David S. Matteson | 2 | 13 | 5.08 |
| John Handley | 3 | 0 | 0.34 |