| Title | Next Generation of Journey Planner in a Smart City |
|---|---|
| ID_Doc | 38462 |
| Authors | Yu, L; Shao, DX; Wu, HY |
| Title | Next Generation of Journey Planner in a Smart City |
| Year | 2015 |
| Published | |
| Abstract | Journey planning is the key to an efficient and sustainable transportation system in a smart city. A good journey planner is expected to help commuters travel safely, comfortably and quickly, as well as keep the whole transportation network running efficiently. In modern cities, it should be able to combine a wide range of private and public transport modes, and more importantly, react to real-time events that are impactful on the topology of the transport network. In this paper, we present our multi-modal journey planner, JPlanner developed for the city of Singapore. JPlanner leverages on more comprehensive urban data, i.e., traffic network data and real-time traffic speed data, aiming to provide more accurate and effective recommendations to commuters. With respect to functionality, JPlanner supports the combination of multiple transport modes, such as Park and Ride for the switch between private car driving and public transport riding. Other travel modes supported by JPlanner include walking, cycling and taxi. We highlight that the key technology enabling the accurate journey planning in JPlanner is the Speed Fusion, which infers real-time traffic speed by fusing different data sources. Finally we use a case study to compare the journey recommendation results between JPlanner and the other two popular journey planners to demonstrate the advantages of our system.. |
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