It sounds like the paper uses a multiple regression model in the form
$$Y = \beta_0 + \sum_i \beta_i \xi_i + \varepsilon$$
where the $\xi_i$ are standardized versions of the independent variables; viz.,
$$\xi_i = \frac{x_i - m_i}{s_i}$$
withe $m_i$ the mean (such as 12.56 in the example) and $s_i$ the standard deviation (such as 9.02 in the example) of the values of the $i^\text{th}$ variable $x_i$ ('buslines' in the example). $\beta_0$ is the intercept (if present). Plugging this expression into the fitted model, with its "betas" written as $\hat{\beta_i}$ (0.275 in the example), and doing some algebra gives the estimates
$$\hat{Y} = \hat{\beta_0} + \sum_i \hat{\beta_i} \frac{x_i - m_i}{s_i}=\left(\hat{\beta_0}-\left(\sum_i\frac{\hat{\beta_i m_i}}{s_i}\right)\right)+\sum_i\left(\frac{\hat{\beta_i}}{s_i}\right)x_i.$$
This shows that the coefficients of the $x_i$ in the model (apart from the constant term) are obtained by dividing the betas by the standard deviations of the independent variables and that the intercept is adjusted by subtracting a suitable linear combination of the betas.
This gives you two ways to predict a new value from a vector $(x_1, \ldots, x_p)$ of independent values:
Using the means $m_i$ and standard deviations $s_i$ as reported in the paper (not recomputed from any new data!), calculate $(\xi_1,\ldots, \xi_p) = ((x_1-m_1)/s_1, \ldots, (x_p-m_p)/s_p)$ and plug those into the regression formula as given by the betas or, equivalently,
Plug $(x_1, \ldots, x_p)$ into the algebraically equivalent formula derived above.
If the paper is using a Generalized Linear Model, you may need to follow this calculation by applying the inverse "link" function to $\hat{Y}$. For example, with logistic regression it would be necessary to apply the logistic function $1/(1 + \exp(-\hat{Y}))$ to obtain the predicted probability ($\hat{Y}$ is the predicted log odds).