income increases. As an “outside good", the utility of home charging is normalized, i.e. $\overline{W}_0^n=0$.

Furthermore, we define $\overline{V}_{j,k}^n$ as follows, $$\begin{aligned}
\overline{V}_{j,k}^n=&
\mu_kd_{j,k}^n+\eta_kz_{j,k}^n+\gamma_kr_{j,k}+\lambda_kg_{j,k}+\delta_km_{j,k},
\end{aligned}$$ where $z_{j,k}^n$ is the destination indicator. If the $j$th charging station is near the EV owner’s travel destination (within a threshold distance $d_{th}$), $z_{j,k}^n=1$, otherwise, $z_{j,k}^n=0$. The term $d_{j,k}^n$ is the deviating distance due to EV charging. We use Dijkstra’s shortest path algorithm [@Algorithm] to calculate the travel route for each EV owner from his/her origin to destination. If an EV owner needs to go to charging station $j$ halfway, we define the deviating distance $d_{j,k}^n$ as the route length of this new route minus the route length of the original route. Additionally, each candidate charging station has a vector of characteristics $[r_{j,k}, g_{j,k}, m_{j,k}]^{\mathrm{T}}$, which characterizes the attractiveness of this charging station in terms of those amenities. For instance, if there exists a restaurant near location $j$, we may set $r_{j,k} = 1$, otherwise we may set $r_{j,k} = 0$. Similarly, $g_{j,k}$ and $m_{j,k}$ are the indicators for shopping center and supermarket, respectively. The corresponding weighting coefficients are $\mu_k,\eta_k,\gamma_k,\lambda_k,\delta_k$.

The EV owner’s choice is not deterministic due to the random unobservable utility. However, we 