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.

/pdf/dynamic-path-planning-algorithms-with-load-balancing-based-79t5plwr2x.pdf

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

A new A star path finding algorithm(named SNA star) was proposed to solve the problem of path finding and optimization by comparing the valuation of shared neighbor nodes, which is based on the reverse improved A star path search algorithm. The characteristics of this algorithm in the paper take the use of the valuation of shared neighbor nodes to verify the search direction, and reversely search from the target to starting, which used the way of local evaluation instead of the way of global evaluation. Taking two simulation experiments as examples to show that the algorithm is effective both on speed and applicability in complex environment. The validity of the algorithm was demonstrated from the comparison and results of the simulation experiment. The evaluation function of this algorithm in the multi-obstacles environment was optimized and evaluation speed was increased at the same time.

Improved reversely a star path search algorithm based on the comparison in valuation of shared neighbor nodes

The path planning for harvesting mechanical arm to avoid obstacle is studied for the working needs based on the known fruits and obstacles information. Firstly, the obstacles were equivalent to point-shaped, round-shaped and line-shaped obstacles according to their growth characteristics in the picking environment, and then they were built model by the boundary feature and done the rasterization in C-space. At last, the smoothing direction-first path planning algorithm based on tree structure was presented, which accomplished the local obstacle avoidance path planning of apple harvesting robot in C-space.

https://ieeexplore.ieee.org/iel7/6621666/6639389/06640428.pdf

Smoothing obstacle avoidance path planning based on C-space for harvesting robot

Pedestrian dynamics model may play an important role in planning and design of public facilities. Based on passenger behavior and movement on bus transit stop, a 2-dimensional cellular automata model for simulation of passenger dynamics was presented. The model introduced the static floor field and dynamic floor field to deal with passenger features of dynamical speed on the basis of pedestrian density and terminal cost change, and could carry out direction choice and movement decision in high density environment. Compared with the existing models, this model is more precise, making its simulation results more realistic.

Modeling and Simulation of Passenger Dynamics on Bus Transit Stop

An improved A* search algorithm was proposed to solve path finding and optimization problems. By altering the search direction and reforming the evaluate function, the undirected finding was transformed to directed finding, making the evaluation to be local. The improved algorithm is efficient especially on solving large-scale path-finding problems. Meanwhile, it avoids dead locks in path-finding. A new way was suggested which separated the search from simulation process to improve large scale simulation. Simulation experiment is shown to demonstrate the feasibility and validity of the algorithm.

Conversely Improved A Star Route Search Algorithm

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Conversely Improved A Star Route Search Algorithm