Dynamic Path Planning Algorithms With Load Balancing Based on Data Prediction for Smart Transportation Systems

In modern transportation, traffic congestion has become an urgent problem in large and medium-sized cities. In smart transportation systems, it is an effective solution to design load balancing path planning algorithms that can dynamically adapt to traffic conditions in order to avoid congestion. In this work, a traffic path planning algorithm based on data prediction (TPPDP) is proposed to find the path with the shortest travel time, which is built on a predictive model based on historical traffic data and current traffic information. Furthermore, a path planning algorithm based on data prediction with load balancing (TPPDP-LB) is also proposed, which combines the predicted information and the number of concurrent requests to achieve the path with shortest travel time while maintaining global load balancing. A specific distributed computing framework for TPPDP-LB algorithm is designed to reduce the runtime of the algorithm. The simulation results proved that both TPPDP and TPPDP-LB algorithms have the advantage of shortest travel time, and TPPDP-LB algorithm achieves load balancing of computing. It is also proved that the distributed computing framework designed for TPPDP-LP algorithm can effectively reduce the runtime of system as well as keep the accuracy of algorithm.

[1]  Yan Peng,et al.  A Dynamic Multipath Scheme for Protecting Source-Location Privacy Using Multiple Sinks in WSNs Intended for IIoT , 2020, IEEE Transactions on Industrial Informatics.

[2]  Guangjie Han,et al.  An Uneven Cluster-Based Mobile Charging Algorithm for Wireless Rechargeable Sensor Networks , 2019, IEEE Systems Journal.

[3]  Hao Wang,et al.  Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDou Navigation Satellite System Data , 2019, IEEE Intelligent Transportation Systems Magazine.

[4]  Xinyu Chen,et al.  A Collaborative Method for Route Discovery Using Taxi Drivers’ Experience and Preferences , 2018, IEEE Transactions on Intelligent Transportation Systems.

[5]  Wang Xiangxue,et al.  Data-Driven Short-Term Forecasting for Urban Road Network Traffic Based on Data Processing and LSTM-RNN , 2018, Arabian Journal for Science and Engineering.

[6]  Yisheng Lv,et al.  Short-term traffic flow prediction with LSTM recurrent neural network , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[7]  Ying Wang,et al.  Mobile robot dynamic path planning based on improved genetic algorithm , 2017 .

[8]  Rong Wang.  A Research on the Weighted Improvement of Dijkstra Algorithm in Optimal Path Calculation , 2017 .

[9]  T. P. Pushphavathi,et al.  Comprehensive traffic management system: Real-time traffic data analysis using RFID , 2017, 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA).

[10]  Feng Xia,et al.  Time-Location-Relationship Combined Service Recommendation Based on Taxi Trajectory Data , 2017, IEEE Transactions on Industrial Informatics.

[11]  Xiangjie Kong,et al.  Urban traffic congestion estimation and prediction based on floating car trajectory data , 2016, Future Gener. Comput. Syst..

[12]  Ji-Rong Wen,et al.  Dynamic Shortest Path Monitoring in Spatial Networks , 2016, Journal of Computer Science and Technology.

[13]  Xiang Li,et al.  TORD Problem and Its Solution Based on Big Trajectories Data , 2016, IEEE Transactions on Intelligent Transportation Systems.

[14]  Zhe Wang,et al.  Hybrid model for prediction of real-time traffic flow , 2016 .

[15]  Gang Wang,et al.  Efficient vehicles path planning algorithm based on taxi GPS big data , 2016 .

[16]  Yingzhong Gu,et al.  Fast Sensitivity Analysis Approach to Assessing Congestion Induced Wind Curtailment , 2015, IEEE Transactions on Power Systems.

[17]  Jian Dai,et al.  Personalized route recommendation using big trajectory data , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[18]  Yu Meng,et al.  Research on the optimal route choice based on improved Dijkstra , 2014, 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA).

[19]  Eleni I. Vlahogianni,et al.  Short-term traffic forecasting: Where we are and where we’re going , 2014 .

[20]  Jianhua Guo,et al.  Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification , 2014 .

[21]  Zhengyu Duan,et al.  Optimal paths planning in dynamic transportation networks with random link travel times , 2014 .

[22]  Lei Chen,et al.  Finding time period-based most frequent path in big trajectory data , 2013, SIGMOD '13.

[23]  N. Mamoulis,et al.  Optimal Route Queries with Arbitrary Order Constraints , 2013, IEEE Transactions on Knowledge and Data Engineering.

[24]  Chung-Cheng Lu,et al.  A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction , 2011 .

[25]  Xing Xie,et al.  Discovering spatio-temporal causal interactions in traffic data streams , 2011, KDD.

[26]  Paolo Santi,et al.  Dynamic route planning in vehicular networks based on future travel estimation , 2010, 2010 IEEE Vehicular Networking Conference.

[27]  Yi Chen,et al.  On the feasibility of UMTS-based Traffic Information Systems , 2010, Ad Hoc Networks.

[28]  Wen Cao,et al.  Applications of the Improved A* Algorithm for Route Planning , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[29]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[30]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[31]  Yingfeng Cai,et al.  Short-Time Traffic State Forecasting Using Adaptive Neighborhood Selection Based on Expansion Strategy , 2018, IEEE Access.

[32]  Xiaojuan Ban,et al.  A Short-Term Traffic Flow Prediction Method Based on Kernel Extreme Learning Machine , 2018, 2018 IEEE International Conference on Big Data and Smart Computing (BigComp).

[33]  Yunpeng Wang,et al.  A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting , 2016 .

[34]  Yu Zheng,et al.  Mining Popular Routes from Social Media , 2015, Multimedia Data Mining and Analytics.

[35]  Yang Lie,et al.  Application of Wavelet Neural Network in Prediction of Short Time Traffic Flow Prediction , 2012 .

[36]  Jiang Feng,et al.  Research Progress on Dynamic Route Planning of Vehicle Navigation , 2010 .

[37]  Sanjay Ghemawat,et al.  MapReduce: simplified data processing on large clusters , 2008, CACM.

[38]  Han Yu.  Conversely Improved A Star Route Search Algorithm , 2007 .

[39]  Hitoshi Kanoh,et al.  Dynamic route planning for car navigation systems using virus genetic algorithms , 2007, Int. J. Knowl. Based Intell. Eng. Syst..

[40]  Shu Lu,et al.  ANALYSIS OF INFLUENCING FACTORS ON URBAN TRAVEL SPEED. , 2003 .