  Electric vehicles (EVs) are increasingly used in transportation. Worldwide
use of EVs, for their limited battery capacity, calls for effective planning of
EVs charging stations to enhance the efficiency of using EVs. This paper
provides a methodology of describing EV detouring behavior for recharging, and
based on this, we adopt the extra driving length caused by detouring and the
length of uncompleted route as the indicators of evaluating an EV charging
station deployment plan. In this way, we can simulate EV behavior based on
travel data (demand). Then, a genetic algorithm (GA) based EV charging station
sitting optimization method is developed to obtain an effective plan. A
detailed case study based on a 100-node 203-branch transportation network
within a 30 km * 30 km region is included to test the effectiveness of our
method. Insights from our method may be applicable for charging station
planning in various transportation networks.
