| Title |
TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data |
| ID_Doc |
44781 |
| Authors |
Chen, C; Jiao, SH; Zhang, S; Liu, WC; Feng, L; Wang, YS |
| Title |
TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data |
| Year |
2018 |
| Published |
Ieee Transactions On Intelligent Transportation Systems, 19, 10 |
| DOI |
10.1109/TITS.2017.2771231 |
| Abstract |
Travel behavior understanding is a long-standing and critically important topic in the area of smart cities. Big volumes of various GPS-based travel data can be easily collected, among which the taxi GPS trajectory data is a typical example. However, in GPS trajectory data, there is usually little information on travelers' activities, thereby they can only support limited applications. Quite a few studies have been focused on enriching the semantic meaning for raw data, such as travel mode/purpose inferring. Unfortunately, trip purpose imputation receives relatively less attention and requires no real-time response. To narrow the gap, we propose a probabilistic two-phase framework named TripImputor, for making the real-time taxi trip purpose imputation and recommending services to passengers at their dropoff points. Specifically, in the first phase, we propose a two-stage clustering algorithm to identify candidate activity areas (CAAs) in the urban space. Then, we extract fine-granularity spatial and temporal patterns of human behaviors inside the CAAs from foursquare check-in data to approximate the priori probability for each activity, and compute the posterior probabilities (i.e., infer the trip purposes) using Bayes' theorem. In the second phase, we take a sophisticated procedure that clusters historical dropoff points and matches the dropoff clusters and CAAs to immerse the real-time response. Finally, we evaluate the effectiveness and efficiency of the proposed two-phase framework using real-world data sets, which consist of road network, check-in data generated by over 38 000 users in one year, and the large-scale taxi trip data generated by over 19000 taxis in a month in Manhattan, New York City, USA. Experimental results demonstrate that the system is able to infer the trip purpose accurately, and can provide recommendation results to passengers within 1.6 s in Manhattan on average, just using a single normal PC. |
| Author Keywords |
Travel behaviour; trip purpose; smart city; Bayes' theorem; trajectory data mining |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000446651100018 |
| WoS Category |
Engineering, Civil; Engineering, Electrical & Electronic; Transportation Science & Technology |
| Research Area |
Engineering; Transportation |
| PDF |
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