TY - GEN
T1 - W-edge
T2 - 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
AU - Stanojevic, Rade
AU - Abbar, Sofiane
AU - Mokbel, Mohamed
PY - 2018/11/6
Y1 - 2018/11/6
N2 - Understanding link travel times (LTT) has received significant attention in transportation and spatial computing literature but they often remain behind closed doors, primarily because the data used for capturing them is considered confidential. Consequently, free and open maps such as OpenStreetMap (OSM) or TIGER, while being remarkably accurate in capturing geometry and topology of the road network are oblivious to actual travel times. Without LTTs computing the optimal routes or estimated time of arrival is challenging and prone to substantial errors. In this work we set to enrich the underlying map information with LTT by using a most basic data about urban trajectories, which also becomes increasingly available for public use: set of origin/destination location/timestamp pairs. Our system, W-edge utilizes such basic trip information to calculate LTT to each individual road segment, effectively assigning a weight to individual edges of the underlying road network. We demonstrate that using appropriately trained edge weights, the errors in estimating travel times are up to 60% lower than the errors observed in OSRM or GraphHopper, two prominent OSM-based, traffic-oblivious, routing engines.
AB - Understanding link travel times (LTT) has received significant attention in transportation and spatial computing literature but they often remain behind closed doors, primarily because the data used for capturing them is considered confidential. Consequently, free and open maps such as OpenStreetMap (OSM) or TIGER, while being remarkably accurate in capturing geometry and topology of the road network are oblivious to actual travel times. Without LTTs computing the optimal routes or estimated time of arrival is challenging and prone to substantial errors. In this work we set to enrich the underlying map information with LTT by using a most basic data about urban trajectories, which also becomes increasingly available for public use: set of origin/destination location/timestamp pairs. Our system, W-edge utilizes such basic trip information to calculate LTT to each individual road segment, effectively assigning a weight to individual edges of the underlying road network. We demonstrate that using appropriately trained edge weights, the errors in estimating travel times are up to 60% lower than the errors observed in OSRM or GraphHopper, two prominent OSM-based, traffic-oblivious, routing engines.
KW - Link travel times
KW - Maps
KW - Ridge regression
UR - http://www.scopus.com/inward/record.url?scp=85058639377&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058639377&partnerID=8YFLogxK
U2 - 10.1145/3274895.3274916
DO - 10.1145/3274895.3274916
M3 - Conference contribution
AN - SCOPUS:85058639377
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 424
EP - 428
BT - 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
A2 - Xiong, Li
A2 - Tamassia, Roberto
A2 - Banaei, Kashani Farnoush
A2 - Guting, Ralf Hartmut
A2 - Hoel, Erik
PB - Association for Computing Machinery
Y2 - 6 November 2018 through 9 November 2018
ER -
