Optimal Allocation of Public Charging Stations based on Traffic Density in Smart Cities

Author(s):  
Miguel Campana ◽  
Esteban Inga
2019 ◽  
Vol 26 ◽  
pp. 101015 ◽  
Author(s):  
Giuseppe Napoli ◽  
Antonio Polimeni ◽  
Salvatore Micari ◽  
Giorgio Dispenza ◽  
Vincenzo Antonucci

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 36039-36049 ◽  
Author(s):  
Youbo Liu ◽  
Yue Xiang ◽  
Yangyang Tan ◽  
Bin Wang ◽  
Junyong Liu ◽  
...  

Author(s):  
Christos G. Cassandras

Poor traffic management in urban environments is responsible for congestion, unnecessary fuel consumption and pollution. Based on new wireless sensor networks and the advent of battery-powered vehicles, this chapter describes three new systems that affect transportation in Smart Cities. First, a Smart Parking system which assigns and reserves an optimal parking space based on the driver's cost function, combining proximity to destination and parking cost. Second, a system to optimally allocate electric vehicles to charging stations and reserve spaces for them. Finally, we address the traffic light control problem by viewing the operation of an intersection as a stochastic hybrid system. Using Infinitesimal Perturbation Analysis (IPA), we derive on-line gradient estimates of a cost metric with respect to the controllable green and red cycle lengths and iteratively adjust light cycle lengths to improve (and possibly optimize) performance, as well as adapt to changing traffic conditions.


2020 ◽  
Vol 12 (18) ◽  
pp. 7343
Author(s):  
Junpeng Cai ◽  
Dewang Chen ◽  
Shixiong Jiang ◽  
Weijing Pan

With the increasing popularization and competition of electric vehicles (EVs), EV users often have anxiety on their trip to find better charging stations with less travel distance. An intelligent charging guidance strategy and two algorithms were proposed to alleviate this problem. First, based on the next destination of EV users’ trip, the strategy established a dynamic-area model to match charging stations with users’ travel demand intelligently. In the dynamic area, the Dijkstra algorithm is used to find the charging station with the shortest trip. Then, the area extension algorithm and the charging station attribution algorithm were developed to improve the robustness of the dynamic area. The two algorithms can automatically adjust the area size according to the number of charging stations in the dynamic area to reduce the number of nodes traversed by the Dijkstra algorithm. Finally, simulation examples were used to verify the effectiveness of the proposed model and algorithms. The results showed that the proposed intelligent charging guidance strategy can meet the travel demand of users. It is a promising technique in smart cities to find better travel trips with less travel distance and less computed time.


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