ABSTRACT
Existing systems for travel time estimation either use data collected from loop detectors and probe vehicle locations, or from GPS traces from cellphones of "online" users. The former methods of data acquisition are expensive, while the latter turns out to be infeasible in connectivity-poor regions. However, many crowdsourced taxi trip datasets (from Boston, Beijing, Rome, etc.) are publicly available which, despite containing limited information, can be made useful for inferring meaningful insights by certain amount of data engineering. The datasets are both cheap to acquire (hence available in large volumes), and impose less heavy connectivity requirements on the end user. One such crowdsourced dataset is the NYC (New York City) Taxi dataset, which contains only the end-point information for each trip. In this paper, a link (road segment) travel time estimation algorithm named Least Square Estimation with Constraint (LSEC) has been developed from such end-point data, which estimates travel time 20% more accurately than existing algorithms. The key idea is to augment a subset of trips with unique paths using logged distance information, as opposed to fitting adhoc "route-choice" models.
- Mohammad Asghari, Tobias Emrich, Ugur Demiryurek, and Cyrus Shahabi. 2015. Probabilistic estimation of link travel times in dynamic road networks. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 47. Google ScholarDigital Library
- Yanying Li. 2008. Short-term prediction of motorway travel time using ANPR and loop data. Journal of Forecasting 27, 6 (2008), 507--517.Google ScholarCross Ref
- Bei Pan, Ugur Demiryurek, and Cyrus Shahabi. 2012. Utilizing real-world transportation data for accurate traffic prediction. In 2012 IEEE 12th International Conference on Data Mining. IEEE, 595--604. Google ScholarDigital Library
- Mahmood Rahmani, Erik Jenelius, and Haris N Koutsopoulos. 2015. Nonparametric estimation of route travel time distributions from low-frequency floating car data. Transportation Research Part C:Emerging Technologies 58 (2015), 343--362.Google ScholarCross Ref
- Irum Sanaullah. 2013. Real-time estimation of travel time using low frequency GPS data from moving sensors. Ph.D. Dissertation. © Irum Sanaullah.Google Scholar
- Philip B Stark and Robert L Parker. 1995. Bounded-variable least-squares: an algorithm and applications. Computational Statistics 10 (1995), 129--129.Google Scholar
- Jameson L Toole, Serdar Colak, Bradley Sturt, Lauren P Alexander, Alexandre Evsukoff, and Marta C Gonzalez. 2015. The path most traveled: Travel demand estimation using big data resources. Transportation Research Part C: Emerging Technologies 58 (2015), 162--177.Google ScholarCross Ref
- Jin Y Yen. 1971. Finding the k shortest loopless paths in a network. management Science 17, 11 (1971), 712--716.Google Scholar
- Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, and Yan Huang. 2010. T-drive: driving directions based on taxi trajectories. In Proceedings of the 18th SIGSPATIAL International conference on advances in geographic information systems. ACM, 99--108. Google ScholarDigital Library
- Xianyuan Zhan, Samiul Hasan, Satish V Ukkusuri, and Camille Kamga. 2013. Urban link travel time estimation using large-scale taxi data with partial information. Transportation Research Part C: Emerging Technologies 33 (2013), 37--49.Google ScholarCross Ref
- Xianyuan Zhan, Satish V Ukkusuri, and Chao Yang. 2015. ABayesian mixture model for short-term average link travel time estimation using large-scale limited information trip-based data. Automation in Construction (2015).Google Scholar
- Fangfang Zheng and Henk Van Zuylen. 2013. Urban link travel time estimation based on sparse probe vehicle data. Transportation Research Part C: Emerging Technologies 31 (2013), 145--157.Google ScholarCross Ref
Index Terms
Link Travel Time Prediction from Large Scale Endpoint Data
Recommendations
A Simple Baseline for Travel Time Estimation using Large-scale Trip Data
Survey Papers and Regular PapersThe increased availability of large-scale trajectory data provides rich information for the study of urban dynamics. For example, New York City Taxi 8 Limousine Commission regularly releases source/destination information of taxi trips, where 173 ...
Travel time estimation of a path using sparse trajectories
KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data miningIn this paper, we propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in real time in a city, based on the GPS trajectories of vehicles received in current time slots ...
Estimating Travel Time of Dhaka City from Mobile Phone Call Detail Records
ICTD '17: Proceedings of the Ninth International Conference on Information and Communication Technologies and DevelopmentTraffic jam in Dhaka city, the capital of Bangladesh and one of the most densely populated cities in the world, is one of the major problems of the commuters. The city dwellers have been experiencing intolerable traffic jam everyday. To reduce the ...
Comments