TY - GEN
T1 - Recommending routes in the context of bicycling
T2 - ACM 2012 Conference on Computer Supported Cooperative Work, CSCW'12
AU - Priedhorsky, Reid
AU - Pitchford, David
AU - Sen, Shilad
AU - Terveen, Loren
PY - 2012
Y1 - 2012
N2 - Users have come to rely on automated route finding services for driving, public transit, walking, and bicycling. Current state of the art route finding algorithms typically rely on objective factors like time and distance; they do not consider subjective preferences that also influence route quality. This paper addresses that need. We introduce a new framework for evaluating edge rating prediction techniques in transportation networks and use it to explore ten families of prediction algorithms in Cyclopath, a geographic wiki that provides route finding services for bicyclists. Overall, we find that personalized algorithms predict more accurately than non-personalized ones, and we identify two algorithms with low error and excellent coverage, one of which is simple enough to be implemented in thin clients like web browsers. These results suggest that routing systems can generate better routes by collecting and analyzing users' subjective preferences.
AB - Users have come to rely on automated route finding services for driving, public transit, walking, and bicycling. Current state of the art route finding algorithms typically rely on objective factors like time and distance; they do not consider subjective preferences that also influence route quality. This paper addresses that need. We introduce a new framework for evaluating edge rating prediction techniques in transportation networks and use it to explore ten families of prediction algorithms in Cyclopath, a geographic wiki that provides route finding services for bicyclists. Overall, we find that personalized algorithms predict more accurately than non-personalized ones, and we identify two algorithms with low error and excellent coverage, one of which is simple enough to be implemented in thin clients like web browsers. These results suggest that routing systems can generate better routes by collecting and analyzing users' subjective preferences.
KW - geographic recommenders
KW - geowikis
KW - recommender systems
KW - route finding
UR - http://www.scopus.com/inward/record.url?scp=84858212664&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84858212664&partnerID=8YFLogxK
U2 - 10.1145/2145204.2145350
DO - 10.1145/2145204.2145350
M3 - Conference contribution
AN - SCOPUS:84858212664
SN - 9781450310864
T3 - Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
SP - 979
EP - 988
BT - CSCW'12 - Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work
Y2 - 11 February 2012 through 15 February 2012
ER -