Abstract
Given a spatial graph, an origin and a destination, and on-board diagnostics (OBD) data, the energy-efficient path selection problem aims to find the path with the least expected energy consumption (EEC). Two main objectives of smart cities are sustainability and prosperity, both of which benefit from reducing the energy consumption of transportation. The challenges of the problem include the dependence of EEC on the physical parameters of vehicles, the autocorrelation of the EEC on segments of paths, the high computational cost of EEC estimation, and potential negative EEC. However, the current cost estimation models for the path selection problem do not consider vehicles’ physical parameters. Moreover, the current path selection algorithms follow the “path + edge” pattern when exploring candidate paths, resulting in redundant computation. Our preliminary work introduced a physics-guided energy consumption model and proposed a maximal-frequented-path-graph shortest-path algorithm using the model. In this work, we propose an informed algorithm using an admissible heuristic and propose an algorithm to handle negative EEC. We analyze the proposed algorithms theoretically and evaluate the proposed algorithms via experiments with real-world and synthetic data. We also conduct two case studies using real-world data and a road test to validate the proposed method.
- Kyoungho Ahn and Hesham A. Rakha. 2013. Network-wide impacts of eco-routing strategies: A large-scale case study. Transport. Res. Part D: Transport Environ. 25, 0 (Dec. 2013). Retrieved from https://trid.trb.org/view/1284524.Google Scholar
- Sabeur Aridhi, Philippe Lacomme, Libo Ren, and Benjamin Vincent. 2015. A MapReduce-based approach for shortest path problem in large-scale networks. Eng. Applic. Artif. Intell. 41 (May 2015), 151--165.Google Scholar
- Andreas Artmeier, Julian Haselmayr, Martin Leucker, and Martin Sachenbacher. 2010. The shortest path problem revisited: Optimal routing for electric vehicles. In KI 2010: Advances in Artificial Intelligence (Lecture Notes in Computer Science). Springer, Berlin, 309--316.Google Scholar
- Andreas Artmeier, Julian Haselmayr, Martin Leucker, and Martin Sachenbacher. 2010. The shortest path problem revisited: Optimal routing for electric vehicles. In Proceedings of the Annual Conference on Artificial Intelligence. Springer, 309--316.Google Scholar
- Jürgen Bang-Jensen and Gregory Z. Gutin. 2008. Digraphs: Theory, Algorithms and Applications. Springer.Google Scholar
- Aaron Brooker, Jeffrey Gonder, Lijuan Wang, Eric Wood, Sean Lopp, and Laurie Ramroth. 2015. FASTSim: A Model to Estimate Vehicle Efficiency, Cost and Performance. SAE Technical Paper 2015-01-0973. SAE International, Warrendale, PA.Google Scholar
- A. Cappiello, I. Chabini, E. K. Nam, A. Lue, and M. Abou Zeid. 2002. A statistical model of vehicle emissions and fuel consumption. In Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems. 801--809.Google Scholar
- B. Y. Chen, W. H. K. Lam, Q. Li, A. Sumalee, and K. Yan. 2013. Shortest path finding problem in stochastic time-dependent road networks with stochastic first-in-first-out property. IEEE Trans. Intell. Transport. Syst. 14, 4 (Dec. 2013), 1907--1917.Google Scholar
- Daniel Delling, Andrew V. Goldberg, Andreas Nowatzyk, and Renato F. Werneck. 2013. PHAST: Hardware-accelerated shortest path trees. J. Parallel Distrib. Comput. 73, 7 (July 2013), 940--952.Google Scholar
Cross Ref
- Yong Deng, Yuxin Chen, Yajuan Zhang, and Sankaran Mahadevan. 2012. Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment. Appl. Soft Comput. 12, 3 (Mar. 2012), 1231--1237.Google Scholar
- Department of Energy. 2016. Energy Department Announces $58 Million to Advance Fuel-Efficient Vehicle Technologies. Retrieved from https://www.energy.gov/articles/energy-department-announces-58-million-advance-fuel-efficient-vehicle-technologies.Google Scholar
- Diane Cook and Len Jenshel. 2017. Buying Guide—Cars and Their Environmental Impact. Retrieved from https://www.nationalgeographic.com/environment/green-guide/buying-guides/car/environmental-impact/.Google Scholar
- E. W. Dijkstra. 1959. A note on two problems in connexion with graphs. Numer. Math. 1, 1 (Dec. 1959), 269--271.Google Scholar
Digital Library
- Daniel Duque, Leonardo Lozano, and Andrés L. Medaglia. 2015. An exact method for the biobjective shortest path problem for large-scale road networks. Eur. J. Oper. Res. 242, 3 (May 2015), 788--797.Google Scholar
Cross Ref
- Jochen Eisner, Stefan Funke, and Sabine Storandt. 2011. Optimal route planning for electric vehicles in large networks. In Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI’11). AAAI Press, 1108--1113.Google Scholar
- EU Horizon 2020 Research and Innovation Programme. 2020. optiTruck. Retrieved from https://optitruck.eu/.Google Scholar
- Yuan Gao. 2011. Shortest path problem with uncertain arc lengths. Comput. Math. Applic. 62, 6 (Sept. 2011), 2591--2600.Google Scholar
- V. M. V. Gunturi, S. Shekhar, and K. Yang. 2015. A critical-time-point approach to all-departure-time Lagrangian shortest paths. IEEE Trans. Knowl. Data Eng. 27, 10 (Oct. 2015), 2591--2603.Google Scholar
- P. E. Hart, N. J. Nilsson, and B. Raphael. 1968. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cyber. 4, 2 (July 1968), 100--107.Google Scholar
Cross Ref
- Heinz Heisler. 2002. 14 - Vehicle body aerodynamics. In Advanced Vehicle Technology (Second Edition), Heinz Heisler (Ed.). Butterworth-Heinemann, Oxford, 584--634. DOI:https://doi.org/10.1016/B978-075065131-8/50015-4Google Scholar
- Xianan Huang and Huei Peng. 2018. Eco-routing based on a data driven fuel consumption model. arXiv:1801.08602 [stat] (Jan. 2018).Google Scholar
- James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers. 2011. Big data: The next frontier for innovation, competition, and productivity. Retrieved from https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation.Google Scholar
- J. Kwon, A. Rousseau, and P. Sharer. 2007. Analyzing the Uncertainty in the Fuel Economy Prediction for the EPA MOVES Binning Methodology. SAE Technical Paper 2007-01-0280. SAE International, Warrendale, PA.Google Scholar
- Yan Li, Pratik Kotwal, Pengyue Wang, Shashi Shekhar, and William Northrop. 2019. Trajectory-aware lowest-cost path selection: A summary of results. In Proceedings of the 16th International Symposium on Spatial and Temporal Databases (SSTD’19). ACM. DOI:https://doi.org/10.1145/3340964.3340971Google Scholar
Digital Library
- Yan Li, Shashi Shekhar, Pengyue Wang, and William Northrop. 2018. Physics-guided energy-efficient path selection: A summary of results. In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL’18). ACM, New York, NY, 99--108. DOI:https://doi.org/10.1145/3274895.3274933Google Scholar
Digital Library
- Paul Newson and John Krumm. 2009. Hidden Markov map matching through noise and sparseness. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS’09). ACM, New York, NY, 336--343. DOI:https://doi.org/10.1145/1653771.1653818Google Scholar
Digital Library
- Daniele Quercia, Rossano Schifanella, and Luca Maria Aiello. 2014. The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city. In Proceedings of the 25th ACM Conference on Hypertext and Social Media (HT’14). ACM, New York, NY, 116--125.Google Scholar
Digital Library
- Christian Sommer. 2014. Shortest-path queries in static networks. ACM Comput. Surv. 46, 4 (Mar. 2014), 45:1--45:31.Google Scholar
Digital Library
- U.S. Energy Information Administration. 2017. International Energy Outlook 2017. Technical Report DOE/EIA-0484(2017). Washington, DC.Google Scholar
- U.S. Energy Information Administration. 2017. Total U.S. energy expenditures in 2015 were the lowest in more than a decade. Retrieved from https://www.eia.gov/todayinenergy/detail.php?id=32432.Google Scholar
- USDOE. 2016. ARPA-E | NEXTCAR. Retrieved from https://arpa-e.energy.gov/?q=arpa-e-programs/nextcar.Google Scholar
- Ram Vijayagopal, Larry Michaels, Aymeric P. Rousseau, Shane Halbach, and Neeraj Shidore. 2010. Automated Model Based Design Process to Evaluate Advanced Component Technologies. SAE Technical Paper 2010-01-0936. SAE International, Warrendale, PA.Google Scholar
- Bin Yang, Jian Dai, Chenjuan Guo, Christian S. Jensen, and Jilin Hu. 2018. PACE: A PAth-CEntric paradigm for stochastic path finding. VLDB J. 27, 2 (Apr. 2018), 153--178.Google Scholar
Digital Library
- L. Zhu, J. Holden, E. Wood, and J. Gonder. 2017. Green routing fuel saving opportunity assessment: A case study using large-scale real-world travel data. In Proceedings of the IEEE Intelligent Vehicles Symposium. 1242--1248.Google Scholar
Index Terms
Physics-guided Energy-efficient Path Selection Using On-board Diagnostics Data
Recommendations
Physics-guided energy-efficient path selection: a summary of results
SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information SystemsGiven a spatial road network, an origin, a destination, and trajectory data of vehicles on the network, the Energy-efficient Path Selection (EPS) problem aims to find the most energy-efficient path (i.e., with least energy consumption) between the ...
A Multiple Pairs Shortest Path Algorithm
The multiple pairs shortest path problem (MPSP) arises in many applications where the shortest paths and distances between only some specific pairs of origin-destination (OD) nodes in a network are desired. The traditional repeated single-source ...
Approximating Shortest Path in Large-Scale Road Networks with Turn Prohibitions Using Multi-constrained Path Algorithm
CIMSIM '13: Proceedings of the 2013 Fifth International Conference on Computational Intelligence, Modelling and SimulationMulti-Constrained Path (MCP) algorithms are path finding algorithms, unlike conventional routing algorithms, they not only give a path between source and destination, also verifies whether the path satisfies the given constraints (Right turn, Left turn ...






Comments