  Nowadays many cities around the world have introduced electric buses to
optimize urban traffic and reduce local carbon emissions. In order to cut
carbon emissions and maximize the utility of electric buses, it is important to
choose suitable routes for them. Traditionally, route selection is on the basis
of dedicated surveys, which are costly in time and labor. In this paper, we
mainly focus attention on planning electric bus routes intelligently, depending
on the unique needs of each region throughout the city. We propose
Clairvoyance, a route planning system that leverages a deep neural network and
a multilayer perceptron to predict the future people's trips and the future
transportation carbon emission in the whole city, respectively. Given the
future information of people's trips and transportation carbon emission, we
utilize a greedy mechanism to recommend bus routes for electric buses that will
depart in an ideal state. Furthermore, representative features of the two
neural networks are extracted from the heterogeneous urban datasets. We
evaluate our approach through extensive experiments on real-world data sources
in Zhuhai, China. The results show that our designed neural network-based
algorithms are consistently superior to the typical baselines. Additionally,
the recommended routes for electric buses are helpful in reducing the peak
value of carbon emissions and making full use of electric buses in the city.
