Data Mining to Improve Planning for Pedestrian and Bicyclist Safety
| dc.contributor.author | Jahangiri, Arash | en |
| dc.contributor.author | Hasani, Mahdie | en |
| dc.contributor.author | Sener, Ipek Nese | en |
| dc.contributor.author | Munira, Sirajum | en |
| dc.contributor.author | Owens, Justin M. | en |
| dc.contributor.author | Appleyard, Bruce | en |
| dc.contributor.author | Ryan, Sherry | en |
| dc.contributor.author | Turner, Shawn M. | en |
| dc.contributor.author | Machiani, Sahar Ghanipoor | en |
| dc.date.accessioned | 2020-01-09T12:51:41Z | en |
| dc.date.available | 2020-01-09T12:51:41Z | en |
| dc.date.issued | 2019-11 | en |
| dc.description.abstract | Between 2009 and 2016, the number of pedestrian and bicyclist fatalities saw a marked trend upward. Taken together, the overall percentage of pedestrian and bicycle crashes now accounts for 18% of total roadway fatalities, up from 13% only a decade ago. Technological advancements in transportation have created unique opportunities to explore and investigate new sources of data for the purpose of improving safety planning. This study investigated data from multiple sources, including automated pedestrian and bicycle counters, video cameras, crash databases, and GPS/mobile applications, to inform bicycle and pedestrian safety improvements. Data mining techniques, a new sampling strategy, and automated video processing methods were adopted to demonstrate a holistic approach that can be applied to identify facilities with highest need of improvement. To estimate pedestrian and bicyclist counts at intersections, exposure models were developed incorporating explanatory variables from a broad spectrum of data sources. Intersection-related crashes and estimated exposure were used to quantify risk, enabling identification of high-risk signalized intersections for walking and bicycling. The modeling framework and data sources used in this study will be beneficial in conducting future analyses for other facility types, such as roadway segments, and also at more aggregate levels, such as traffic analysis zones. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/10919/96338 | en |
| dc.language.iso | en | en |
| dc.publisher | SAFE-D: Safety Through Disruption National University Transportation Center | en |
| dc.relation.ispartofseries | SAFE-D;01-003 | en |
| dc.rights | CC0 1.0 Universal | en |
| dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | en |
| dc.subject | high-risk signalized intersections | en |
| dc.subject | exposure modeling | en |
| dc.subject | direct demand models | en |
| dc.subject | pedestrian and bicyclist safety | en |
| dc.subject | data mining | en |
| dc.title | Data Mining to Improve Planning for Pedestrian and Bicyclist Safety | en |
| dc.type | Report | en |
| dc.type.dcmitype | Text | en |