ABSTRACT
This paper instantly infers the gas consumption and pollution emission of vehicles traveling on a city's road network in a current time slot, using GPS trajectories from a sample of vehicles (e.g., taxicabs). The knowledge can be used to suggest cost-efficient driving routes as well as identifying road segments where gas has been wasted significantly. The instant estimation of the emissions from vehicles can enable pollution alerts and help diagnose the root cause of air pollution in the long run. In our method, we first compute the travel speed of each road segment using the GPS trajectories received recently. As many road segments are not traversed by trajectories (i.e., data sparsity), we propose a Travel Speed Estimation (TSE) model based on a context-aware matrix factorization approach. TSE leverages features learned from other data sources, e.g., map data and historical trajectories, to deal with the data sparsity problem. We then propose a Traffic Volume Inference (TVI) model to infer the number of vehicles passing each road segment per minute. TVI is an unsupervised Bayesian Network that incorporates multiple factors, such as travel speed, weather conditions and geographical features of a road. Given the travel speed and traffic volume of a road segment, gas consumption and emissions can be calculated based on existing environmental theories. We evaluate our method based on extensive experiments using GPS trajectories generated by over 32,000 taxis in Beijing over a period of two months. The results demonstrate the advantages of our method over baselines, validating the contribution of its components and finding interesting discoveries for the benefit of society.
Supplemental Material
- Greenshields, B. D., Bibbins, J. R., Channing, W. S., and Miller, H. H. 1935. A study of traffic capacity. In Highway research board proceedings (Vol. 14).Google Scholar
- Gühnemann, A., Schäfer, R. P., Thiessenhusen, K. U., and Wagner, P. 2004. Monitoring traffic and emissions by floating car data. Institute of Transport Studies Working Paper, (ITS-WP-04-07).Google Scholar
- Helbing, D. 2001. Traffic and related self-driven many-particle systems. Reviews of modern physics, 73(4), 1067.Google Scholar
- Honicky, R., Brewer, E. A., Paulos, E., and White, R. 2008. N-smarts: networked suite of mobile atmospheric real-time sensors. In Proc. of the second ACM SIGCOMM workshop on Networked systems for developing regions (pp. 25--30). Google ScholarDigital Library
- Kwon, J., and Murphy, K. 2000. Modeling freeway traffic with coupled HMMs. Technical report, Univ. California, Berkeley.Google Scholar
- Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., and Huang, Y. 2009. Map-matching for low-sampling-rate GPS trajectories. In Proc. of ACM SIGSPATIAL GIS, 352--361. Google ScholarDigital Library
- Muñoz, L., Sun, X., Horowitz, R., and Alvarez, L. 2003. Traffic density estimation with the cell transmission model. In Proc. of the 2003 American Control Conference, Vol. 5, 3750--3755.Google Scholar
- Ntziachristos, L., Samaras, Z., Eggleston, S., Gorissen, N., Hassel, D., and Hickman, A. J. 2000. COPERT III. Computer Programme to calculate emissions from road transport, methodology and emission factors (version 2.1). European Energy Agency (EEA), Copenhagen.Google Scholar
- Panis, L.I., Broekx, S., and Liu, R. 2006. Modelling instantaneous traffic emission and the influence of traffic speed limits. Science of the total environment, 371(1), 270--285.Google Scholar
- Schäfer, R. P., Thiessenhusen, K. U., and Wagner, P. 2002. A traffic information system by means of real-time floating-car data. In ITS world congress (Vol. 2).Google Scholar
- Smit, R., Ntziachristos, L., and Boulter, P. 2010. Validation of road vehicle and traffic emission models--A review and meta-analysis. Atmospheric environment, 44(25), 2943--2953.Google Scholar
- Steed, A., Spinello, S., Croxford, B., and Greenhalgh, C. 2003. E-Science in the streets: urban pollution monitoring. In UK e-science all hands meeting.Google Scholar
- Wilkie, D., Sewall, J., and Lin, M. 2013. Flow reconstruction for data-driven traffic animation. ACM Trans. on Graphics, 32(4), 89. Google ScholarDigital Library
- Xie S., Song X., and Shen X. 2006. Calculating Vehicular Emission Factors with COPERT? Mode in China. Environmental Science, 27(3), 415--419.Google Scholar
- Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., and Huang, Y. 2010. T-drive: driving directions based on taxi trajectories. In Proc. of the 18th SIGSPATIAL GIS, 99--108. Google ScholarDigital Library
- Zhang, Y., Roughan, M., Willinger, W., and Qiu, L. 2009. Spatio-temporal compressive sensing and internet traffic matrices. In ACM SIGCOMM Computer Communication Review, 39(4), 267--278. Google ScholarDigital Library
- Zhu, Y., Li, Z., Zhu, H., Li, M., and Zhang, Q. 2013. A compressive sensing approach to urban traffic estimation with probe vehicles. Mobile Computing, IEEE Transactions on, 12(11), 2289--2302. Google ScholarDigital Library
- Zou, H. X., Yue, Y., Li, Q. Q., and Yeh, A. G. O. 2011. Traffic data interpolation method of non-detection road link based on Kriging interpolation. Jiaotong Yunshu Gongcheng Xuebao, 11(3), 118--126.Google Scholar
- Zheng, Y., Capra, Li, Wolfson, O., Yang, H. 2014. Urban computing: concepts, methodologies, and applications. ACM Trans. On Intelligent systems and Technology, 5(3). Google ScholarDigital Library
- Data released: http://research.microsoft.com/apps/pubs/?id=217455Google Scholar
Index Terms
- Inferring gas consumption and pollution emission of vehicles throughout a city
Recommendations
Real-Time City-Scale Taxi Ridesharing
We proposed and developed a taxi-sharing system that accepts taxi passengers' real-time ride requests sent from smart phones and schedules proper taxis to pick up them via ride sharing, subject to time, capacity, and monetary constraints. The monetary ...
Implementation of Vehicle Occupancy and Aggregate Emission Rate
SCA '18: Proceedings of the 3rd International Conference on Smart City ApplicationsTransportation is considered as one of the most important elements contributing on the ecological degradation due to vehicular emissions, which are related to the highest congestions. This paper studies the relationship between the vehicle occupancy and ...
PrivateHunt: Multi-Source Data-Driven Dispatching in For-Hire Vehicle Systems
Recently, for-hire vehicle services (FHV, e.g., Uber and Lyft) have become essential to people's daily transportation. Similar to taxis, how to effectively dispatch these FHV based on demand and supply is important for both FHV passengers and drivers. ...
Comments