Paper Info
Title
Next Generation of Journey Planner in a Smart City.
Abstract
Journey planning is the key to an efficient andsustainable transportation system in a smart city. A good journeyplanner is expected to help commuters travel safely, comfortablyand quickly, as well as keep the whole transportation networkrunning efficiently. In modern cities, it should be able to combinea wide range of private and public transport modes, and moreimportantly, react to real-time events that are impactful on thetopology of the transport network. In this paper, we present ourmulti-modal journey planner, JPlanner developed for the cityof Singapore. JPlanner leverages on more comprehensive urbandata, i.e., traffic network data and real-time traffic speed data, aiming to provide more accurate and effective recommendationsto commuters. With respect to functionality, JPlanner supportsthe combination of multiple transport modes, such as \"Park andRide\" for the switch between private car driving and publictransport riding. Other travel modes supported by JPlanner include walking, cycling and taxi. We highlight that the keytechnology enabling the accurate journey planning in JPlanner isthe Speed Fusion, which infers real-time traffic speed by fusingdifferent data sources. Finally we use a case study to compare thejourney recommendation results between JPlanner and the othertwo popular journey planners to demonstrate the advantages ofour system.
Year
DOI
Venue
2015
10.1109/ICDMW.2015.12
ICDM Workshops
Keywords
Field
DocType
next generation,smart city,journey planning,sustainable transportation system,transportation network running,private transport modes,public transport modes,multimodal journey planner,JPlanner leverages,urban data,traffic network data,real-time traffic speed data,multiple transport modes,private car driving,public transport riding,cycling,taxi,journey planners
Computer science,Simulation,Transport engineering,Planner,Public transport,Artificial intelligence,Smart city,Traffic network,Transportation planning,Machine learning,Transport network
Conference
Citations 
PageRank 
References 
3
0.45
5
Authors
3
Name
Order
Citations
PageRank
Yu Liang12112.01
Dongxu Shao2313.69
Huayu Wu318422.70