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text:
\section{Introduction}
By $2050$, it is expected\cite{nations2014world} that $66$\% of the World population will live in urban environments, with many of our cities set to become {\em megacities}. Hence, also driven by the {\em Internet of Things} (IoT) revolution and the explosion in the amount of available data, many governments and local authorities have adopted (or are in the process of adopting) concepts based on Smart Cities to tackle the key challenges related to the growing intensive urbanization\cite{bigdata}. As a result, much research and development effort is currently devoted to the design of IoT and data-driven services with the goal of making Smart Cities sustainable\cite{SILVA2018697}, capable of improving the quality of life of their Citizens with subsequent societal, economic and environmental impacts. The design of Intelligent Transportation Systems (ITSs) is central to achieve each of these goals and an exciting opportunity is offered by the possibility of designing, and deploying, services leveraging the increasing pervasiveness of connected cars. \\
In this context, we propose the principled design of a data-driven service based on crowdsourcing for the routing of connected cars, CRAWLING (CRowdsourcing Algorithm on WheeLs for smart parkING). Connected cars equipped with CRAWLING are able to crowdsource the skills/services they need in order to fulfill a given routing task. The service is also designed to allow cars equipped with CRAWLING to gather data from other sources, such as other cars, pedestrians, smart sensors or social media. As a result, vehicles equipped with CRAWLING become an active part of a sharing economy\cite{Sharing_book} ecosystem, where vehicles share, and create, {\em skills} that can be used within the service. A key feature of our service is that it is built around a framework that intrinsically allows to consider stochastic behaviors,
thus explicitly accounting for users' stochasticity and to capture their privacy requirements. Specifically, CRAWLING exploits a recent data-driven control algorithm\cite{russo2020crowdsourcing,garrabe2021design} that returns a randomized behavior for the car (i.e., a probability function) by solving a sequential decision-making problem\cite{GARRABE_2022b} that formalizes the tracking of a desired behavior expressed in probabilistic terms. That is, our service systematically minimizes a cost function consisting of: (i) a reward capturing road/traffic conditions; (ii) a regularizer that biases the solution towards some target behavior, allowing CRAWLING to keep into account the possible preferences of passengers. \\
While CRAWLING can be tailored towards generic routing problems, for concreteness we focus here on the problem of routing cars to find parking spaces. Smart parking allocation is attracting much research attention\cite{fog_parking, blockchain_privacy, QL_parking,8467499}, as roaming for parking is known to be a major source of pollution, traffic and user stress\cite{roman_overko_2021_6410603,Jemmali:2022aa,FAHIM2021e07050}. In a broader context, lack of coordination in transportation systems is believed to be correlated to an increase in circulating vehicles\cite{coord_uber}. To mitigate this, in the context of parking management, urban sensors can be used to monitor the availability of parking spaces and the key challenge becomes that of designing algorithms that are able to  optimally route the flow of vehicles\cite{dahleh_motion}. Currently, many such algorithms rely on deterministic decision-making\cite{route_planning_review}. For example, methods\cite{multiDepotVRP} for solving multi-depot vehicle routing frame the problem via deterministic optimization, using binary decision variables. On the other hand, the use of stochastic frameworks has been gaining traction in the Intelligent Transportation Systems (ITSs) literature, for example in the context of differential privacy where privacy is achieved by injecting noise in the system\cite{10.1007/11761679_29}, or in driver\cite{driver_intent} or pedestrian\cite{pedestrian_prediction} intent prediction. Efforts to leverage users' data in Smart City contexts are also the focus of much attention\cite{bigdata1,bigdata2}, for example for energy\cite{DDelectricity} or taxi\cite{DDtaxi} demand prediction, or congestion analysis\cite{congestion_data}. While such efforts often focus on modelling\cite{mod} and/or artificial intelligence methods\cite{DDia, ML_subway}, some specialized stochastic optimization frameworks have also arisen, for example in parking assignment\cite{stochastic_parking_assignment}. Finally, data-driven ITSs, and the data-driven analysis of ITSs\cite{minimum_parking} have also gained  traction, although heterogeneous datasets, emerging from different information streams that need to be merged, have been singled out as a challenge\cite{DDITS}.  As an example of one such stream, social media monitoring has been recently used to identify trends related to traffic obstructions\cite{crowd_sensing} and monitor traffic in real time\cite{twitter_traffic}.\\
In summary, the key contributions of the paper can be summarized as follows: after formalizing the smart parking problem as a data-driven control problem, we propose the principled design of CRAWLING and present a stand-alone, general-purpose, implementation of the service. The service provably returns the optimal behavior for cars on which it is equipped. Then, we evaluate its computation times and discuss how these are suitable for routing applications. We also show, via simulations leveraging the microscopic traffic simulator SUMO\cite{SUMO2018}, the effectiveness of CRAWLING in orchestrating the use of parking spaces in a University Campus during rush hours in the morning. Simulations show that CRAWLING effectively directs the connected cars,  confirming that cars equipped with our service improve their average time-to-parking, avoiding unfavourable areas of the map and reacting online to environmental changes (reported, in the scenarios we consider, via social media). \\
The paper is organized as follows. In Section \ref{sec:methods}, we begin by introducing the data-driven control framework exploited by CRAWLING, and subsequently describing its design, the implementation and the scenarios used to assess its performance. Then, in Section \ref{sec:results}, we report the results obtained with the service. We do so by first evaluating the computational efficiency and then discussing the performance obtained on the reference scenarios, which were specifically chosen to validate all the key features of the service. Finally, we give concluding remarks, and discuss the relevance of the results, Section \ref{sec:discussion}.

\subsection{Notation}\label{sec:notation}
We note the set of road links, or lanes in what follows, as $\mathcal{X}$. Time is discrete and, at time step $k$, the connected car occupies link $\mathbf{x}_k\in\mathcal{X}$. The probability of a connected car of going from $\mathbf{x}_{k-1}$ to $\mathbf{x}_k$ is denoted by $\pi(\mathbf{x}_k|\mathbf{x}_{k-1})$. The car has available a number of services (or sources) and the $i$-th service/source is  denoted by $\pi^{(i)}(\mathbf{x}_k|\mathbf{x}_{k-1})$. The target behavior of the car represents the behavior that passengers would like to track in the absence of road (or environmental) disruptions: this is denoted by $p(\mathbf{x}_{k}|\mathbf{x}_{k-1})$. Finally, the reward obtained by the car at time $k$ for being on $\mathbf{x}_{k}$ is denoted by $r_k(\mathbf{x}_k)$. As we shall see, the reward is used in our service to capture road/environmental disruptions that might not be known to the vehicle passengers. Also, we denote by  $\pi(\mathbf{x}_1,\ldots,\mathbf{x}_N|\mathbf{x}_0)$ and $p(\mathbf{x}_1,\ldots,\mathbf{x}_N|\mathbf{x}_0)$ the product of the transition probabilities $\pi(\mathbf{x}_k|\mathbf{x}_{k-1})$ and $p(\mathbf{x}_{k}|\mathbf{x}_{k-1})$, respectively. These joint probability functions clearly depend on the initial condition of the car, $\mathbf{x}_0$, i.e. the starting link/lane of the trip. Finally, we let: (i) $\mathbb{E}_p[h(\mathbf{X})] := \sum h(\mathbf{x})p(\mathbf{x})$ be the expectation of $h(\cdot)$ with the sum taken over the support of the probability function $p(\mathbf{x})$; (ii) the Kullback-Leibler (KL) divergence\cite{KL_51} of $p(\mathbf{x})$ with respect to $q(\mathbf{x})$, with $p(\mathbf{x})$ absolutely continuous with respect to $q(\mathbf{x})$ is  $\mathcal{D}_{\text{KL}}\left(p(\mathbf{x}) \mid\mid q(\mathbf{x}) \right):= \sum p(\mathbf{x}) \log\frac{p(\mathbf{x}) }{q(\mathbf{x}) } $ and measures the proximity of the pair $p(\mathbf{x})$, $q(\mathbf{x})$. We use the Matlab-like notation $k_1:k_2$ to denote the set of integers between the integers $k_1$ and $k_2$ (included). Consequently, $\{w_k\}_{k_1:k_2}$ denotes the set $\{w_{k_1},\ldots,w_{k_2}\}$.

\begin{figure}[h!]
    \centering
    \includegraphics[width=0.75\columnwidth]{archiv4.PNG}
    \caption{CRAWLING functional architecture and its interactions with the environment. CRAWLING is an in-car service and consists of a decision-making (DM) algorithm, a cost module and a sampling mechanism. These components compute the route the car should take based on environmental data and on the available services.}
    \label{fig:architecture}
\end{figure}

\section{Materials and methods}\label{sec:methods}

We now introduce CRAWLING. We first present the algorithm (Section \ref{sec:service}) and then discuss its implementation (Section \ref{sec:code}) and the reference scenarios used to assess its performance (Section \ref{sec:scenarios}). The key functional components of CRAWLING, and its interactions with the surrounding road environment, are schematically illustrated in Figure \ref{fig:architecture}. As shown in such a figure, CRAWLING is an in-car service: it gathers online data from the environment to feed a suitably defined cost function and has available a number of source services (or simply sources in what follows) to route the car. The sources are e.g., specialized routing services as well as directions collected from other cars in the area also equipped with CRAWLING. Based on the cost and the available sources, CRAWLING dynamically determines the next direction the car should take. This is done via a decision-making (DM) algorithm that exploits recent results in the context of data-driven control\cite{russo2020crowdsourcing,garrabe2021design}. The DM algorithm returns a probability function (i.e., a policy) capturing the turning probabilities of the car. The next road link that the car should take is then obtained by sampling form the policy.

\subsection{CRAWLING: description of the service}\label{sec:service}

CRAWLING seeks to determine the optimal turning probabilities, say $\tilde \pi(\mathbf{x}_k|\mathbf{x}_{k-1})$, of the car by orchestrating the use of a set of available sources. The goal is to track a target behavior for the car, $p(\mathbf{x}_k|\mathbf{x}_{k-1})$, while simultaneously maximizing the car-specific reward $r_k(\mathbf{x}_k)$. The target behavior can be used to capture a preferred set of directions for the car passengers. This can be computed from e.g., historical data\cite{route_pred,8317888} and does not depend on the road conditions. These can be instead captured through the car-specific reward $r_k(\mathbf{x}_k)$. Within the service, the reward signal is used to capture data that are not included in the target behavior: typically, these are related to the state of the network, including hazardous conditions, traffic and roadworks (Scenario $1$ described in Section \ref{sec:scenarios}). Such data can be gathered from a network of urban sensors and the reward can also be fed by interactions with social media (Scenario $2$ in Section \ref{sec:scenarios}). For example, in the simulations from Section \ref{sec:results}, a component of the reward favours road links adjacent to an available parking space, encouraging agents to seek such spaces and park. This structure allows the reward to encompass specifications linked to various tasks, meaning CRAWLING is not per se limited to parking management. To introduce our service, say $S$ the number of available sources, i.e.,  $\pi^{(i)}(\mathbf{x}_k|\mathbf{x}_{k-1})$ with $i=1,\ldots,S$ (see Section \ref{sec:notation} for the definitions). These are assumed to be such that $\mathcal{D}_{\text{KL}}\left(\pi^{(i)}(\mathbf{x}_k\mid \mathbf{x}_{k-1})\mid\mid p(\mathbf{x}_k\mid \mathbf{x}_{k-1})\right) < +\infty$. This is a standing assumption for our service that has been shown to be non-restrictive in practice\cite{garrabe2021design}. Given this set-up, the problem tackled by CRAWLING to determine the optimal route for the car can be formalized as follows\cite{russo2020crowdsourcing,garrabe2021design}:
\begin{Problem}\label{prob:problem}
Let $N$ be a positive integer, $\tilde{r}_k(\mathbf{x}_{k-1}) := \mathbb{E}_{\pi(\mathbf{x}_k\mid\mathbf{x}_{k-1})}\left[r_k(\mathbf{X}_k)\right]$ and $\pi(\mathbf{x}_0)$, $p(\mathbf{x}_0)$ be priors on the initial conditions. Find the sequence of weights ${\left\{\boldsymbol{\alpha}_k^\ast\right\}_{1:N}}$, with $\boldsymbol{\alpha}^{\ast}_k:=[\alpha_k^{(1)},\ldots,\alpha_k^{(S)}]$ being an $S$-dimensional vector at time step $k$, solving
\begin{equation}\label{eqn:problem}
    \begin{aligned}
    \underset{\left\{ \boldsymbol{\alpha}_k\right\}_{1:N}}{\text{min}}   &\mathcal{D}_{\text{KL}}\left(\pi(\mathbf{x}_1,\ldots,\mathbf{x}_N|\mathbf{x}_0)\pi(\mathbf{x}_0)||p(\mathbf{x}_1,\ldots,\mathbf{x}_N|\mathbf{x}_0)p(\mathbf{x}_0)\right) - \sum_{k=1}^N\mathbb{E}_{\pi(\mathbf{x}_{k-1})}\left[\tilde{r}_k(\mathbf{X}_{k-1})\right]\\
    s.t. & \ \pi(\mathbf{x}_{k}|\mathbf{x}_{k-1}) = \sum_{i\in1:S}\alpha_k^{(i)}\pi^{(i)}(\mathbf{x}_{k}|\mathbf{x}_{k-1}), \ \ \ \forall k \\
    & \sum_{i\in1:S}\alpha_k^{(i)} = 1, \ \ \alpha_k^{(i)} \in \{0, 1\},   \ \ \forall k.
    \end{aligned}
\end{equation}
\end{Problem}
In Problem \ref{prob:problem} the cost function formalizes the fact that the goal of CRAWLING is to track the target behavior while maximizing the car-specific reward. The constraints capture the fact that the solution of the problem is a probability function and, in turn, this probability function is determined by picking one of the sources available to CRAWLING. The problem is tackled\cite{russo2020crowdsourcing,garrabe2021design} by Algorithm \ref{alg:crowd}, which embeds both the {\em cost} and {\em DM algorithm} components of the service in Figure \ref{fig:architecture}. The key macro-steps of the algorithm, which outputs the optimal solution to Problem \ref{prob:problem}, are described next.

The inputs to the algorithm (lines $1-6$) are, besides reward and sources, a time horizon, $N$, and the current state of the agent, $\mathbf{x}_{k-1}$. The target behavior is an optional input parameter. Indeed, if not provided to CRAWLING, this is set to the uniform distribution (lines $7-9$). We note that, when this happens, the first term in the cost of the problem in (\ref{eqn:problem}) becomes an entropic regularizer, also widely used in the literature on reinforcement learning\cite{maxent_levine}. The output of the algorithm (lines $10-11$) are the optimal turning probabilities throughout the time horizon. This is essentially the plan that the car will follow. Following the initialization phase, the for loop computes the \emph{reward-to-go} for the car (this is computed by the {\em cost} module in Figure \ref{fig:architecture}) which represents how \emph{promising} a given direction plan is for the car. The reward-to-go is computed via a backward recursion that starts from the last time step in the time horizon.  Intuitively, the time horizon is a measure of \emph{how far in the future} CRAWLING can look to make a decision (see Section \ref{sec:computation} for the corresponding computational aspect). Specifically, in Algorithm \ref{alg:crowd}, the reward signal $r_k(\cdot)$ received by CRAWLING is updated with information from the future time steps through the signal $\hat{r}_k(\cdot)$, which is obtained via backward recursion. Then, the cost of each source's policy is calculated (lines $17 - 18$) and the source incurring the smallest cost is selected, and used as the agent's policy (i.e., turning probabilities) -- this is returned in line $23$. Finally, the next link that the car needs to take is determined by sampling from the {\em Sampling} component illustrated in Figure \ref{fig:architecture}: this component, given the current state of the car, samples from the next turning probability determined within Algorithm \ref{alg:crowd}.

\begin{algorithm}[!t]
\begin{algorithmic}[1]
\caption{CRAWLING in-car service}\label{alg:crowd}
{
\State {\bf Inputs:}
    \State Current state -- $\mathbf{x}_{k-1}$
	\State Time horizon -- $N$
	\State Reward -- $r_k(\mathbf{x}_k)$
	\State Sources -- $\{\pi^{(i)}(\mathbf{x}_k|\mathbf{x}_{k-1})\}_{1:N}$, with $i\in 1:S$
 	\State Target behavior (optional) -- $\{p(\mathbf{x}_k|\mathbf{x}_{k-1})\}_{1:N}$

  	\vspace{0.2cm}
    \If {Target behavior not provided}
    \State $\{p(\mathbf{x}_k|\mathbf{x}_{k-1})\}_{1:N}\gets \text{uniform distribution}$
  \EndIf
	\vspace{0.2cm}
	\State {\bf Output:}
	\State Optimal turning probabilities $\{\tilde\pi(\mathbf{x}_k|\mathbf{x}_{k-1})\}_{1:N}$
	\vspace{0.2cm}
	\State {\bf Initialization:}
	\State Set the vectors $\mathbf{a}^T_{N+1}(\mathbf{x}_N)\gets 0$ and $\tilde{\boldsymbol{\alpha}}_{N+1}\gets 0$
	\vspace{0.2cm}
	\For {$k = N$ to $1$}
	\State 	$\hat{r}_k(\mathbf{x}_k) \gets \mathbf{a}^T_{k+1}(\mathbf{x}_k)\tilde{\boldsymbol{\alpha}}_{k+1}$
	\State $\bar{r}_k(\mathbf{x}_k)\gets {r}_k(\mathbf{x}_k) - \hat{r}_k(\mathbf{x}_k)$
	\For {$i=1:S$}
	\State $a_k^{(i)}(\mathbf{x}_{k-1})\gets\mathcal{D}_{\text{KL}}\left(\pi^{(i)}(\mathbf{x}_k|\mathbf{x}_{k-1})||p(\mathbf{x}_k|\mathbf{x}_{k-1})\right)+\mathbb{E}_{\pi^{(i)}(\mathbf{x}_k|\mathbf{x}_{k-1})}\left[\bar{r}_k(\mathbf{X}_k)\right]$
	\EndFor
	\State $j_k \gets \underset{i}{\text{argmin }} a_k^{(i)}(\mathbf{x}_{k-1})$
	\State $\mathbf{a}_k(\mathbf{x}_{k-1})\gets [a_k^{(1)}(\mathbf{x}_{k-1}),\ldots,a_k^{(S_k)}(\mathbf{x}_{k-1})]^T$
	\State ${\boldsymbol{\alpha}}_k^{\ast} \gets [\alpha_k^{(1)},\ldots,\alpha_k^{(S_k)}]^T$, where $\alpha_k^{(i)}=0$, $\forall i \ne j_k$ and $\alpha_k^{(j_k)}=1$
	\State $\tilde{\pi}(\mathbf{x}_k|\mathbf{x}_{k-1})\gets \pi^{(j_k)}(\mathbf{x}_k|\mathbf{x}_{k-1})$
	\EndFor
	}
\end{algorithmic}
\end{algorithm}

Before reporting on the implementation of CRAWLING, we also highlight here a benefit in computing (and using as input) probability functions. First, the sources used by CRAWLING are intrinsically stochastic and this can be useful when the agents generating these data want to e.g. guarantee some desired level of differential privacy. This privacy mechanism consists in corrupting the information sent by the source with some (typically) Laplacian noise. Hence, it can be captured with a source having a Laplacian probability function. Moreover, Algorithm \ref{alg:crowd} does not determine the next state of the car but rather a probability function. This aspect can be used to guarantee some level of privacy for drivers using CRAWLING, making it possible to both privately share their policy (which can become a source for other vehicles equipped with CRAWLING) and determine the next direction by sampling from this probability.

\subsection{CRAWLING implementation and validation set-up}\label{sec:code}
We now discuss the proposed implementation for CRAWLING. The code can be found at \url{https://tinyurl.com/wcwvcwaj}. The key functional components of the validation set-up are illustrated in Figure \ref{fig:setup}. As shown in such a figure, we validate our service via the microscopic traffic simulator SUMO\cite{SUMO2018}. This serves as environment (in which real maps can be imported and traffic demand can be generated) with which CRAWLING interacts. Interactions with SUMO happen via the Python interface TraCI: this is used to both gather the environmental data from SUMO and to control the vehicles based on the directions provided by Algorithm \ref{alg:crowd}. Moreover, the {\em Social Interface} module gives CRAWLING the possibility of monitoring certain social media (in our scenarios of Section \ref{sec:scenarios}, Twitter). In the set-up we propose, this is done by using the Python library Tweepy (https://www.tweepy.org/), which allows to retrieve a given user's tweet history.

\begin{figure}
    \centering
    \includegraphics[width=0.75\linewidth]{architecture2.png}
    \caption{CRAWLING validation set-up. CRAWLING is implemented in Python and interacts with SUMO. This provides the back-end simulation from which CRAWLING gathers the relevant data through TraCI. The directions obtained via Algorithm \ref{alg:crowd} are fed back to the simulation environment via the interface TraCI. For example, in the validation scenarios described in Section \ref{sec:scenarios}, CRAWLING receives data such as parking availability or obstructed road links, which are used to compute directions to connected cars equipped with our service. CRAWLING also interfaces via a Social Interface module with information on social media (in our second scenario, Twitter).}
    \label{fig:setup}
\end{figure}
In the CRAWLING implementation, the discrete time steps are not uniform but are rather associated to the vehicle changing link (i.e., when the vehicle transitions state). This design solution removes the need for the cars to have a synchronized clock. Algorithm \ref{alg:crowd} is then implemented via a receding horizon strategy, thus allowing to handle possible changes in the environment. Specifically, every time (say, $t-1$) a given car equipped with CRAWLING transitions to a new link the algorithm:
\begin{itemize}
    \item gathers the available data and sources (in the setting of Figure \ref{fig:setup} this is done by parsing the data from SUMO through TraCI and from Twitter via Tweepy);
    \item builds the reward based on new information that might have become available during the transition to the new link;
    \item computes the optimal plan of actions for the next $N$ links according to Algorithm \ref{alg:crowd};
    \item determines the next direction by sampling from  $\tilde\pi(\mathbf{x}_{t}|\mathbf{x}_{t-1})$. The car is then controlled through TraCI by setting the direction obtained  from the sampling.
\end{itemize}

In our code (see our gitub repository for the details) connected cars are encoded by an \emph{agent} Python class, which contains information about the user's goal (i.e., the route/destination the driver vehicle would like to follow/attain). Algorithm \ref{alg:crowd} used by CRAWLING also has a Python class, which takes an agent as argument (i.e., a vehicle). This class regroups all the methods used for the algorithm, namely the computation of the KL divergence, a receding horizon control loop and a sampling mechanism. A simulation file is also provided to manage the key interfaces between the agent, CRAWLING and the simulator used to assess its performance (i.e, SUMO). Specifically, the simulation file includes functions for updating the state of the CRAWLING vehicle in the simulation, updating the reward and performing simulation steps through the SUMO-Python interface TraCI. The simulation file for Scenario $1$ (see Section \ref{sec:scenarios}) also logs results when the simulation is over, while the simulation file for Scenario $2$ (see Section \ref{sec:scenarios}) contains the necessary code to interface CRAWLING with social media, namely Twitter. The file implements a parsing function that, monitoring specific users (one of the authors in this case), is called at every step. If the tweet parsed by our code has the hashtag '\#sumo\_experiment' it is considered for parsing. In this case, the function splits the string into individual words and searches for the word 'blocked' (we leave the design of a more refined parser for future research) and for a road link identifier. Then, the date of the tweet is examined to ensure the tweet has an impact on the current trip: if the tweet was posted before the current date it is ignored. Finally, if all the conditions are met, the blocked road link is assigned a very negative reward as described in Section \ref{sec:sim}.

\begin{figure}
    \centering
    \includegraphics[width=0.75\linewidth]{unisa_map.png}
    \caption{Campus road network. Highways and parking lots ate represented by their respective international symbols.}
    \label{fig:unisa_map}
\end{figure}

\subsection{Scenarios description}\label{sec:scenarios}
We now describe the scenarios (Scenario $1$ and Scenario $2$ in what follows) that are used to validate CRAWLING. The two scenarios were selected to validate different aspects of our service. Namely, with Scenario $1$ we wanted to verify the effectiveness of CRAWLING in smoothly managing a flow of cars that, without our service, could possibly create road blocks. As such, this scenario is centered around the management of a fleet of cars entering the University of Salerno campus during morning rushes. Scenario $2$ was instead aimed at verifying how CRAWLING would adapt to incoming information about road closures made available via a Twitter account.

\noindent \textbf{Scenario $1$.} This scenario simulates a morning rush hour at the campus of the University of Salerno. The campus of the University of Salerno, shown in Figure \ref{fig:unisa_map} is served by two highway exits, and has three parking lots (in our simulations, each parking lot can accommodate up to $50$ cars). The highway exits and corresponding campus entrances will be referred to as southern and northern entrance and the parking lots as Multipiano, Terminal and Biblioteca parking lots (see Figure \ref{fig:unisa_map} for the position of such parking lots on the campus map). In this scenario, a fleet of $150$ cars (i.e., the maximum number of cars that can be accommodated in the simulation) arrives on campus before the teaching begins, with students and faculty staff searching for parking spaces. In particular, $100$ cars arrive from the northern highway exit and seek to park on either Terminal or Biblioteca, while the $50$ remaining cars arrive from the south and reach the Multipiano parking lot. The cars arrive on campus one by one at $15$-second intervals, with the order of arrival being shuffled. That is, when each car arrives, its destination on campus and whether it is equipped with CRAWLING or not, are randomly selected. While cars arrive to campus, roadworks are being carried out on the main ramp leading from the northern highway exit to the campus, rendering traffic on the corresponding links (highlighted in blue in Figure \ref{fig:scenar_osbtru}) difficult (the vehicles using such links have their speed restricted to less than one kilometer per hour). This ramp also corresponds to the preferred route of uncontrolled cars (that is, cars not equipped with CRAWLING) that seek to park in Terminal. In Section \ref{sec:results} we use this scenario to benchmark the effectiveness of CRAWLING in properly managing the flow of cars. Simulations with varying amounts of controlled cars are performed to quantify performance.

\noindent \textbf{Scenario $2$.} In the second scenario, only two cars are considered. They both enter the campus from the northern highway exit, at an interval of $30$ seconds, and seek to reach the Biblioteca parking lot. Both cars are equipped with CRAWLING. Within the scenario, the ramp leading directly from the highway exit to the parking lot is closed. Specifically, after the first car passes through the exit, the ramp is shut off completely and this information is shared through Twitter (by e.g., pedestrians in the area or the local public authority). This information is gathered by CRAWLING and leveraged to re-route the second car entering in the simulation, to find an alternative route that would properly allow it to park in a suitable parking space.

\section{Results}\label{sec:results}
We now describe the results obtained with CRAWLING on the two scenarios of Section \ref{sec:scenarios}. To do so, we first empirically investigate the computational load incurred by  CRAWLING (and discuss our design choices to make the algorithm suitable for routing applications) and then we describe the main settings of our simulation framework. The code can be accessed at \url{https://tinyurl.com/wcwvcwaj}. Note that, to replicate our simulations, a working installation of SUMO, TraCI, Tweepy and the main scientific computing Python libraries is required, along with a Twitter account with developer access.

\subsection{Computational performance}\label{sec:computation}

To make CRAWLING suitable for in-car operation, we leverage the fact that cars being on a given link (i.e., state $\mathbf{x}_{k-1}$) can only transition to an outgoing link (that is, $\mathbf{x}_{k}$ can only be a link in the set of outgoing neighbors of $\mathbf{x}_{k-1}$).  This means that the support of the pfs $\pi(\mathbf{x}_{k}|\mathbf{x}_{k-1})$, $p(\mathbf{x}_{k}|\mathbf{x}_{k-1})$ and $\pi(\mathbf{x}_{k}|\mathbf{x}_{k-1})$ is restricted to the outgoing neighboring links of $\mathbf{x}_{k-1}$. Hence, only the neighbors of $\mathbf{x}_{k-1}$ need to be considered in the computations of Algorithm \ref{alg:crowd}. Thus, in the algorithm implementation: (i) the set of outgoing neighbors associated to the current link of the car is determined; (ii) computations are subsequently performed only over the neighborhood (which depends on the time horizon) rather than over the full state space (which would amount to the full map). Using this approach, we empirically investigated if the service is fast enough for in-car applications.  We did this by running  CRAWLING on each link of the University Campus map in Figure \ref{fig:unisa_map} and logging the average running time. On this map, each road link is connected, on average, to $7$ other links. We repeated these simulations by varying, across them, both the amount of data sources available to the decision-maker (i.e., $S$ was gradually increased between $1$ and $6$) and the time horizon (i.e., $N$ was gradually increased between $0$ and $5$). The results are summarized in Figure \ref{fig:comput}, leftward panel. The figure illustrates that the computation time is linear with respect to the number of sources (Figure \ref{fig:comput}, middle panel). This is due to the fact that Algorithm \ref{alg:crowd} iterates once per source at each time step. More interestingly, the computation time is approximately exponential with respect to the time horizon (Figure \ref{fig:comput}, rightward panel). This is aspect is essentially due to the exponential increase in the state space (even if reduced as described above) as the time horizon, $N$, increases.

\begin{figure}
    \centering
    \includegraphics[width=0.33\columnwidth]{3dplot_2811.png}
    \includegraphics[width=0.33\columnwidth]{sourceplot.png}
    \includegraphics[width=0.33\columnwidth]{thorplot.png}
    \caption{CRAWLING execution time as a function of available sources and time horizon. Leftward panel: computation time (in seconds) as a function of both time horizon and number of services. Middle panel: computation time (in seconds) as a function of the time horizon with $6$ available sources. Rightward panel: computation time (in seconds) as a function of the amount of sources for a time horizon of $5$ steps ahead.}
    \label{fig:comput}
\end{figure}

\subsection{Simulation settings}

We now report the settings that are used to implement the scenarios of Section \ref{sec:scenarios} and to obtain the results of Section \ref{sec:sim}.

\noindent \textbf{Scenarios settings.} The parameters are set before running the simulation. In what follows we simply say that vehicles equipped with CRAWLING are {\em controlled} and vehicles without the service are {\em uncontrolled}. First, we created lists containing: (i) the starting points and target parking lots for uncontrolled vehicles; (ii) the target behavior for controlled vehicles. These lists are then shuffled to randomize the departing order of the vehicles in the simulation. For simulations involving both controlled and uncontrolled vehicles, the indexes of controlled and uncontrolled vehicles are randomly assigned. All this information is stored as .npy files. Namely: for Scenario $1$, the files {\em agent.npy} and {\em foe.npy} contain this information. These files are generated before each simulation to avoid biased results. This can be done using the notebook {\em Simulation launcher.ipynb}. The relevant maps were imported from OpenStreetMap (https://www.openstreetmap.org/) using the netconvert software and cleaned up using netedit. A full tutorial for this procedure can be found in the SUMO online documentation. At the beginning of each simulation, SUMO loads the map, while TraCI adds each car from the .npy files to the simulation. The sources, stored as numpy arrays, are loaded when CRAWLING is launched. While SUMO runs the simulation, information including vehicle data and network conditions is obtained in TraCI and transmitted to CRAWLING, which builds the reward.

\noindent \textbf{Car settings.} All cars are implemented as TraCI agents. Controlled cars query CRAWLING for directions each time they transition to a new link, while uncontrolled cars are assigned a pre-defined trajectory at the beginning of the simulation (the trajectory is determined by the built-in SUMO routing function that determines the shortest path to the destination). Uncontrolled cars are set to automatically park at the end of their course if possible. On the other hand, controlled cars are assigned a parking space if they arrive to a non-full parking lot. If the parking lot is full, both controlled and uncontrolled cars are rerouted by assigning them a new, non-full parking lot (for uncontrolled cars this is done by assigning them a new destination in SUMO, while controlled agents are assigned a new target behavior).

\begin{figure}
    \centering
    \includegraphics[width=0.75\linewidth]{Screenshot.png}
    \caption{Northern section of the campus with obstructions highlighted in blue for Scenario $1$. Map imported from OpenStreetMap in SUMO via its netconvert tool}
    \label{fig:scenar_osbtru}
\end{figure}

\subsection{Results}\label{sec:sim}

We now describe the simulation specifics and report the results.

\noindent \textbf{Reward and behaviors settings.} The reward encompasses environmental disruptions. In our use-case, this is computed from the state of the road links and is related to the availability of parking spaces. Specifically, at each time step, each link adjacent to a parking lot is assigned a reward of $100$ if the parking lot has vacant spaces, and $-10$ if not. Road links on which traffic is perturbed are assigned an additional negative reward: (i) $-20$ if the traffic is heavily slowed down on the link. This has been simulated, as in Scenario $1$, by enforcing a speed limit of less than one kilometer per hour on the perturbed road links; (ii) $-100$ if the road link is blocked altogether (as in Scenario $2$). Before running the simulations, a set of sources is compiled by using SUMO's routing function: each source is designed to represent directions leading to a different point in the network. We build $6$ sources, using the highway exits, the parking lots and  the north-western side of the campus as destinations. This ensures that the directory of behaviors available to the decision-maker provides a wide coverage of directions from each lane. For each road link, the probability functions $\pi^{(i)}(\mathbf{x}_k\mid\mathbf{x}_{k-1})$ of the sources are built by assigning a high probability to the lane indicated by the routing while the other neighbor links have a small, uniform, probability of being selected. We also built a last source by merging behaviors of cars routed towards the Biblioteca and Terminal parking lot. This source can be thought of as emulating the traces from previous vehicles having navigated the campus. In the following, the target behavior of each connected car is selected among the sources.\\
\noindent \textbf{Scenario $1$ specifics.} Our first set of simulations follows the first scenario outlined in Section \ref{sec:scenarios}. We implemented this scenario by simulating $150$ cars arriving at the campus at $15$-second intervals (with a simulation lasting approximately one hour and twenty minutes of simulated time) and seeking to park. All cars entering the campus via the Southern exit are directed towards the Multipiano parking lot. Uncontrolled cars entering through the Northern exit all seek to park in the Terminal parking lot. On the other hand, the target behavior of controlled cars entering through the Northern exit leads to either Biblioteca or Terminal (this can be interpreted as a target behavior being built from traces of previous CRAWLING cars having similar goals), highlighting the interest of obtaining directions through crowdsourcing stochastic policies. However, the road leading from the northern entrance to the roundabout immediately to its East is obstructed, causing traffic to significantly slow down (as described above, the speed limit on such road links is reduced to less than one kilometer per hour). As specified in Section \ref{sec:scenarios}, this obstruction occurs on the preferred route of uncontrolled cars directed towards Terminal, that is, uncontrolled cars within the $100$ cars entering through the Northern highway exit. This is illustrated in Figure \ref{fig:scenar_osbtru}, which captures a section of the full campus map. Such obstructions can originate from road work, or model a limited capacity of the road link related to e.g. severe climatic conditions.
In the results shown next, we run different set of simulations with $0$, $50$, $100$ and $150$ controlled cars. This was done to evaluate the {\em penetration} rate of the service, showing its effectiveness as the number of cars equipped with CRAWLING increases.

\noindent \textbf{Results for Scenario $1$.} As shown in Figure \ref{fig:simres}, the fully uncontrolled fleet (no vehicles equipped with CRAWLING) fails to park every agent in the allocated time. Indeed, all uncontrolled cars arriving from the northern highway exit are routed to the obstructed lanes. On the other hand, when the fleet is fully controlled (with all vehicles being equipped with our service), CRAWLING only needs approximately half the duration of the simulation to park every car. Namely, CRAWLING allows each controlled cars to adapt its route to avoid the obstruction, effectively orchestrating the use of the available parking spaces. Specifically, we observed that connected cars entering from the Northern exit were routed to the Biblioteca parking lot until it became full. When this happened, CRAWLING rerouted the remaining cars to the Terminal parking lot by a detour route avoiding the obstructed road links. Both controlled and uncontrolled cars entering from the southern highway exit reach the Multipiano parking lot, as it is the closest.
\begin{figure}
    \centering
    \includegraphics[width=0.75\linewidth]{plot_sumo.png}
    \caption{Confidence interval plot of the simulation results. Solid lines represent the mean value, while the shaded area is the standard deviation. Fully controlled fleets manage to fully park within half of the simulation time, while fleets with less controlled cars take longer, failing to find parking spaces for every car before the simulation ends in the extreme case. Note that fleets with just one third of the cars using CRAWLING still significantly outperform the baseline of uncontrolled cars.}
    \label{fig:simres}
\end{figure}
To complement our analysis, we also recorded the average time spent by the average car on an obstructed link and the average time-to-parking over all simulations. These are shown in Table \ref{tab:simres}, together with the results from Welch's t-test\cite{welch_test}, which we used to verify the statistical significance of our simulations. The test is aimed at checking whether two random samples originate from distributions having different means. In other words, a p-value less than $0.05$ would mean that a set of simulations is statistically insignificant and needs to be rejected. Specifically, we use the p-value to compare the average parking time obtained in the five first simulations performed with a set of parameters with the five last simulations following the same parameters.
\begin{table}[]
    \centering
\begin{tabular}[t]{lccc}
\hline
    Scenario & Time (s) spent on obstruction & Average time-to-parking (s) & p-value\\
    \hline
    Fully uncontrolled & 754.2 & 2142.5 & 0.06\\
    50 controlled & 613.1 & 1999.0 & 0.63\\
    100 controlled & 280.2 & 1464.6 & 0.76\\
    150 controlled & 4.1 & 1186.2 & 0.46\\
    \hline
\end{tabular}
    \caption{Summary table for the numerical experiments. All times in seconds.}
    \label{tab:simres}
\end{table}
Table \ref{tab:simres} confirms the qualitative behaviors shown in Figure \ref{fig:simres}. In particular, the increase in the number of cars equipped with CRAWLING decreases both the time spent on obstruction and the time-to-parking. This is due to the fact, with more cars being controlled via CRAWLING, these were able to coordinate to avoid the unfavorable road link, while being directed towards a parking with space with effective capacity to accommodate the incoming cars.

\noindent \textbf{Scenario $2$ specifics.} For the second scenario in Section \ref{sec:scenarios} we considered two controlled cars entering in the campus from the North entrance, both with the goal of reaching the western parking lot. However, after the first car exits the highway, the road is being blocked. This information, which causes the reward associated to the corresponding road link to sharply decrease, is {\em twitted}. Specifically, from one of the authors' Twitter profile, the following tweet is made: \emph{North highway ramp blocked $\#$sumo\_experiment} after the first car's passage. Periodically, our implementation of CRAWLING checks the Twitter account's post history. The last tweet is parsed and, if an accident is detected -- see Section \ref{sec:code} for a description of the parsing mechanism -- CRAWLING assigns a very negative (i.e., $-100$) reward to the road link where the disrupting event is signalled.\\
\noindent \textbf{Results for Scenario $2$.} The results are summarized in Figure \ref{fig:snapshots}. Such a figure illustrates, in panel (a), the key campus areas for the scenario. At the beginning of the scenario, no road obstruction is detected and indeed, as shown in panel (b), the first car entering in the simulation normally transitions through the Northern exit, following the shortest path through the ramp. After the car passes the ramp, the road obstruction occurs (the obstructed link is highlighted in blue in panel (c)) and the obstruction is reported via a tweet (in this experiment, from one of the authors, see panel (d)). Panel (d) showcases the corresponding tweet that was issued upon detection of the road blocking. Then, panel (e) shows the result of CRAWLING adapting to the new information. Indeed, the route for the second car is re-computed by our service to avoid that the second car enters in the obstructed road link. This is a result of CRAWLING detecting the reward change due to the tweet. An interesting question is to determine a measure of trust for the tweets to be used by CRAWLING. We leave this topic for our future research. At the end of the simulation -- panel (f) -- both cars achieve their goal by reaching their desired parking lot. A video of the corresponding simulation is also available at \url{https://tinyurl.com/wcwvcwaj}.

\begin{figure}
    \centering

    \includegraphics[width=0.75\linewidth]{panel_a.png}\\
    \includegraphics[width=0.75\linewidth]{panels_b_d.png}\\
    \includegraphics[width=0.75\linewidth]{panels_e_f.png}\\
    \caption{Snapshots taken from the simulation for Scenario $2$. Panel (a): areas of the campus interested by the use-case. Specifically, the red rectangles denote the position of the snapshots on the map. Panel (b): first car taking the fastest route from the Northern entrance when there is no obstruction. Panel (c): the route is blocked after the first car has passed the Northern entrance. Panel (d): the information about the disrupting event is shared via a tweet. Panel (e): CRAWLING adapts to handle the disruption and the second car takes an alternate route. Panel (f): both cars reach the desired parking lot.}
    \label{fig:snapshots}
\end{figure}

\section{Discussion and concluding remarks}\label{sec:discussion}
We presented the principled design of CRAWLING, an in-car service for smart parking, for which we make our code available. The service is based on a recent data-driven control algorithm, where the cost function has a Kullback-Leibler divergence component, ensuring that the car tracks a certain target behavior, i.e. a route that the user would like to take. A reward signal is also incorporated into the cost, compiling additional information that is collected through a network of urban sensors and on social media. A key feature of our service is that it determines turning probabilities for cars. Hence, the {\em policy} that is computed by the service is stochastic and it allows to both embed in the framework the possibility that drivers do not follow the indications provided by CRAWLING and to guarantee some desired level of (differential) privacy for users participating to the service. After describing the key functional components of CRAWLING, we proposed a stand-alone, general-purpose implementation of our service and investigated its effectiveness on two Smart Cities scenarios, leveraging SUMO for microscopic traffic simulations. With our first scenario (morning rush) we investigated what happens as the number of vehicles equipped with CRAWLING increases, empirically finding that parking times can be considerably improved even when only $30\%$ of the vehicles were controlled by our service. These simulations were also performed with a limited amount of sources, providing encouraging insights into the system's ability to efficiently make decisions based on a limited set of available services. With our second validation scenario, we investigated the effectiveness of CRAWLING to adapt to  environmental changes reported via social media: simulations once again confirmed the benefits of equipping cars with our service.

In a broader context, a number of considerations can be drawn from our findings. While for concreteness we tailored CRAWLING towards parking management, its approach can be exported to other routing applications. We see our findings as a first step to implement an {\em internet of skills} for connected cars. In this framework, we envisage cars to collaboratively exchange learned skills so as to create a collective intelligence enabling cars to augment each others' abilities through crowdsourcing. We also note that some ventures have already started delivering services based on data collaboratively shared by their users, see for example the Waze application\cite{sharing_economy_mobility}. Interestingly, similar mechanisms based on crowdsourcing can be found in neuroscience -- see, for example the recent {\em thousand brains} theory\cite{1000Brains}, which postulates that the neocortex in our brain does not compute brand new {\em behavioral models} but it patches simpler models from the cortical columns. Finally, in the context of reinforcement learning, a mechanism related to CRAWLING has been recently proposed to orchestrate the use of learning-based and model-based policies to tackle certain control tasks. Interestingly, it has been shown that mechanisms orchestrating these heterogeneous policies can be designed so that they can tutor each other and this, in turn, improves learning efficiency and performance\cite{10.1007/978-3-030-55180-3_6,9654881,pmlr-v168-lellis22a}. Inspired by these studies, possible future directions of our research might involve both embedding tutoring mechanisms in CRAWLING and exploring the links between the approach we presented and certain recent neuroscience theories. We will also embed strategies to filter social media information for their effective use in CRAWLING. Finally, we aim at investigating whether CRAWLING can represent an effective mechanism to fairly regulate possible competitive behaviors between vehicles equipped with our service and legacy, manually-driven, vehicles when these compete for the same, limited, resource.

\section*{Author contributions statement}
G.R. designed the research. E.G. implemented the service, developed the simulations and assessed the numerical methods. Both authors evaluated and discussed the numerical results and contributed in writing the paper.

\section*{Additional information}

\textbf{Data availability}\\
The code, and the corresponding detailed instructions, needed to replicate the simulations can be found at: \url{https://tinyurl.com/wcwvcwaj}\\
\textbf{Competing interests}\\
The authors do not declare any competing interest.

redpajama_set_name: RedPajamaArXiv


text:
Lawmakers Want To Up Schools' Mental Health Funds
Filed Under:Kathy Sheran, Mankato, Mass Shootings, Mental Health, Mental Health Care, Minnesota's National Alliance on Mental Illness, Schools, Sue Abderholden
ST. PAUL, Minn. (AP) — While state and federal politicians consider changes to gun laws, a group of Minnesota lawmakers want to improve mental health care in schools.
Sen. Kathy Sheran unveiled a bill Friday that would give schools an extra $10 million over the next two years to bring in mental health specialists. The bill would also provide more training to mental health professionals handling psychotic episodes, and resources for earlier treatment of teens experiencing a crisis.
Sheran says mental health is one factor in mass shootings that the state needs to address. The Mankato Democrat and colleagues say schools are the best place to catch mental illness before a student becomes violent.
Sue Abderholden, executive director of Minnesota's National Alliance on Mental Illness, says only 5 percent of the mentally ill are violent.
(© Copyright 2013 The Associated Press. All Rights Reserved. This material may not be published, broadcast, rewritten or redistributed.)

redpajama_set_name: RedPajamaCommonCrawl


What's your brother like? To Miao, her brother Fen is someone who would tease her and make her explode 100 times a day; not a peaceful day as the two are fighting every single minute. However, when troubles come, Fen will become a caring, loving brother who protects his little sister at all cost.


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text:
dLife > Diabetes 101 > Famous People with Diabetes
Famous People with Diabetes: Drew Carey
Claim to fame: actor, comedian, game show host
DOB: May 23, 1958
Diabetes type: 2
Quote: "I'm Drew Carey, and yes, I have lost weight. Thanks for asking."
Drew Carey was a marching-band trumpet and coronet player at Rhodes High School. He enrolled at Kent State University after graduating high school in 1975, joined the Delta Tau fraternity, and became an avid board-game player. Still battling depression, Carey had difficulty with focus, and couldn't decide on a major. By his junior year of college, Carey had been expelled twice from Kent State, and attempted suicide at least once.
With the aim of pursuing a career as a stand-up comedian, Carey dropped out of college. His depression worsened as he traveled across the country, trying his luck in the stand-up business. He attempted suicide again in his early twenties, and returned home to Cleveland to voraciously consume self-help books.
Carey signed himself up for the Marine Corps Reserve in 1980, believing the armed forces would provide the structure he had been craving. It was there that Carey developed mentally and physically during his six-year tenure. This period also helped to develop Carey's signature look: thick black standard-issue glasses and a military buzz cut.
Childhood friend, actor, and radio personality David Lawrence contacted Carey in 1986, asking Carey to help him write comedy bits for a morning radio show. Carey gained more confidence because of his contributions to the show, and as a result, he began to make the rounds on the local comedy circuit. Only a few months later, he won a competition at the Cleveland Comedy Club, and began working as their regular emcee. He made two notable appearances on the TV talent show Star Search the following year. Carey continued to perform as much as possible over the next few years, often traveling between Ohio and Los Angeles. In 1991, he made a celebrity-making appearance on The Tonight Show, where he earned the respect of host Johnny Carson.
ABC gave Carey a shot at his own sitcom—The Drew Carey Show—which debuted in 1995 and was a ratings favorite. In 1997, Carey published his autobiography, Dirty Jokes and Beer: Stories of the Unrefined, which chronicled his hard-partying, adult-entertainment-enjoying side. The book helped establish Carey's bad-boy persona, and became a New York Times bestseller list mainstay. In 1998, Carey began hosting Whose Line Is It, Anyway?—a wildly popular improv show that featured comedians like Ryan Stiles, Wayne Brady, and Colin Mochrie. Carey took the show's members on the road in 2001, billing the group as "The Improv All-Stars."
In 2002, Carey was chosen as the host for the White House Correspondents' Dinner, and in 2004, The Drew Carey Show wrapped up, followed shortly by the ending of Whose Line Is It, Anyway? in 2006. During this downtime, Carey developed a passion for sports photography, specifically in the area of soccer. Then, in 2007, Carey was tapped to helm The Power of 10, the CBS game show, and was handpicked to replace Bob Barker as the host of the long-running The Price is Right. Carey proposed to his girlfriend, Nicole Jaracz, that same year.
In 2010, while announcing to the world that he felt he was a good fit for the host of The Price is Right, Carey revealed a healthier version of himself after dropping eighty pounds. He was able to stop taking diabetes medication after his weight loss. At fifty-two years old, Carey had gone from a size forty-four pants to a size thirty-three/thirty-four with diet and exercise.
Exclusive Interview With Ryan Reed: Talking About Racing With Diabetes
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Famous People with Diabetes: Mahalia Jackson


It's frighting to think what could happen if your child or a burglar gets their hands on your firearm(s). Using a secure gun safe is one of the easiest and best things you can do to improve you and your family's safety.
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The Liberty Safe Lincoln Series is one of the best looking safes we have seen. Incorporates Liberty's signature shelving construction which keeps your safe organized. Key specs include: 6730 mechanical lock, 15 Bolt, 11 Gauge steel, and 2.5 Hours fire resistance at 1700 degrees, 1035 lbs.
What Makes a Good Gun Safe?
It is obliviously important to want a safe that effectively protects your firearms and other possessions from burglars, children, and fires. However some safes are much better at doing these than others, which are simply marketed well but really don't deliver.
UL RSC (Residential Security Container) – This is a certificate that is given to gun safes which are given once tested by Underwriters Laboratories (UL). This rating indicates the security of the safe when faced against forced entry. Different grades of the certificate indicate how secure a safe is.
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Just because the gun safe manufacturer claims it has high security, a "secure" locking system, with reinforced steel, it doesn't mean it can't be broken into with simply a pry bar, or even a screwdriver. Even if the safe has a thick metal door if it has flaws elsewhere i.e. in its locking system, then it really isn't going to keep your firearms and other belongings safe.
Other things to look for is if it has anchor holes (to bolt it down), quality of welding, door gap (to prevent easy prying), lock security, and fire rating. The safes we have recommended in these gun safe reviews have many of these features, if not all.
Does paying more for a safe mean better quality? Not always. Many safes are marketed to look "secure" with fancy features, but when it comes to actual security they are weak, and you are really only paying for a better looking safe.
You may also want to consider other features a safe might have, such as: adjustable shelving, door pockets, power in, ethernet, and digital locking. Many safes are now also incorporating biometrics, but these can be unreliable on cheaper safes.
There are a lot of great safes available and equally poor ones. The key thing to look for is if they have been tested by reputable organization to ensure they can really back up their claims.
If you enjoyed these gun safe reviews we have a number of more specific gun safe reviews you can checkout using the links below.
If you own at least one firearm, you need a gun safe. In certain states, such as California, not owning a gun safe means you potentially face liability should your firearm be stolen. Not all gun safes are created equally, so it can be hard to choose the right one for your needs.
Lots of research is needed to separate the facts from marketing claims to ensure you end up with a gun safe that is not only affordable and practical, but will serve its ultimate purpose. There are no regulatory standards placed upon gun safes in the USA. Your gun safe will likely be a lifetime investment, so get it right the first time.
Why Use a Gun Safe?
Gun safes not only help protect your firearms from theft, but they can also ensure the guns are safely kept away from children, reducing the likelihood of misuse inside the home. If you live in a state that requires guns to be stored in some sort of safe, it is a good idea to buy the safe at the same time or shortly after you buy a firearm.
There are multiple types of guns safes, with safes classified based on the type of gun being stored, or the type of locking mechanism used.
Also known as fingerprint gun safes, these safes use a locking system that requires fingerprints to open them. Because you can program several fingerprints, you are able to give multiple people access to the safe. The major benefit to this kind of safe is that it can be opened in a moments notice. You have to be careful when you buy one because some of the products available are not made with the highest quality of standards. Pay attention to the brand and make sure to buy a reputable one.
Rather than using a key or combination, the safe opens using a code entered on a digital dial pad. These offer a quick convenience, but you may find they run on a battery, so you must have a backup battery supply to ensure it functions properly all the time. It is also important to change the code on a regular basis since dial pads will begin to wear with time. Signs of wear may hint to others what the code could be, thus allowing unauthorized access to the guns in the safe.
One of the most reliable types of safe is the dial safe. Because it does not use technology that is prone to failure, this safe offers security. A dial combination is required to open the door. Though secure, it is not easy to open at a moments notice compared to other types of safes available today. You cannot change the combination on this type of safe. No power supply is required, unless there are features such as lighting or dehumidifiers that need power to function.
These safes are smaller because they are designed to hold pistols and handguns. Some of these are even small enough to fit inside a drawer, which makes them ideal. If you only have a few valuables to store inside the safe and you don't have any long guns that you need to store, a handgun safe may be the ideal option. These are typically less expensive, though many people opt to invest in a long gun safe so they have room to grow their collection.
Larger and more bulky, these safes are designed to store larger sized guns, as well as multiple guns. They are available in a number of sizes depending on the number of guns you need to store. Many also include other compartments to allow for storage of gun accessories, such as cleaning supplies. Some safes even feature compartments that allow you to store other valuables. They can also be used for handguns, especially if you use a rack to keep them organized.
Every gun owner will have different needs when it comes to their safe. Inspect different locking mechanisms, explore different brands and features, and look at price points to determine what will work best for you.
It is a good idea to buy a gun safe that is larger than what you think you need. You never know when you will want to add to your collection, so you need to be sure you have extra room for any additional firearms that may be added to your collection later. In addition to protecting your guns, the right safe can protect other valuables as well. You can quickly fill up the safe, and space will likely go faster than you realize. Measure all the items you intend to store in the safe, and add a bit more room when you shop around, to be safe.
While you want a gun safe that will provide you room to grow, you also want to keep it as small as possible, to make it easier to store and to conceal. If the safe becomes obvious, it is a target for burglary. Ideally, it should be kept in the basement or attic, but could also be kept in a closet.
Though thicker steel will make the overall unit heavier – the thicker the steel, the better the protection you'll have. Thicker steel will also cost you more, so this is something to consider. 12-gauge steel is only about 0.081 inches thick, which can easily be broken into with power tools. It is best to opt for 10-gauge, or lower, with 8 being ideal, for the sides and doors.
Those who have time to wait for a bargain can usually find a used gun safe for less than $1,000. Lighter units, weighing 500 pounds or less, you can move on your own. If the unit is heavier, expect to pay for moving and installation.
Buying new means you'll spend an average of at least $800, with many American made models starting at $1,000. Unless you ship and install the safe on your own, add another 10 to 30% to cover shipping and installation costs.
Gun safes that cannot easily be defeated with brute force methods will run anywhere from $1,400-$5,000 brand-new, depending on size and fire protection. These safes offer a tremendous amount of protection against all non-power tools. However, power tools will still get inside in a matter of minutes to hours depending on installation and location. Because these are heavier, they cost more to move, so add and 50% for shipping and installation.
To get a top quality true safe that will stand up against power tools, prepare to spend anywhere between $2,000 to $15,000, depending on the size, level of security, and fire protection. These can weigh more than a ton, so add anywhere from 20% to 80% more to cover the cost of shipping and installation.
Though the budget is important, also take time to consider the level of warranty and support that comes with the safe at the time of purchase. Read the fine print, and be aware that not all safes will come with one. If you purchase a safe with fingerprint scanners or electronic locks, these components could fail over time. If a locksmith ever needs to come open or repair the safe, there could a potentially substantial cost involved. If the warranty covers these issues, it could be worth the extra expense when initially buying the safe. If not, the money you save by opting out of a warranty could cost you later.
If you cannot afford the safe that offers the protection you desire, you can do things on your own to make a less expensive safe better.
Select a good location – out of plain view – and bolt the safe to the floor. Consider hiding the safe altogether – perhaps inside a hollowed out refrigerator. Customize your gun safe with extra steel, only in locations where you need it.
Improve overall home security and reduce fire risk. Fireproof the safe yourself, or install a sprinkler head directly over the safe's location. Keep your personal tools locked up, or hidden, so as to prevent burglars from using your own tools to break into the safe. Don't store ammunition or powder in the safe, as these could ignite in a fire, and make the situation worse. Store it separately, in another container, away from sources of heat and flame.
Practice "free" home security techniques, such as forwarding your phone calls when you leave, or using timed lights, so you appear home when you're not to avoid becoming a burglar target.
Spend the money on a safe that offers the size, features, and protection you want. Your gun collection may easily add up to tens of thousands of dollars in value, so why protect it with ta safe that only cost a few hundred dollars? If budget is a concern, opt for a used safe. Compared to many other products, safes typically do not wear out over time. Look for businesses in your area that are changing locations; you can secure a high quality commercial safe for not much more than the cost of removing it.
Spend more on a safe that offers fire resistance. Pay close attention to the claims of the safe, as fireproofing may not be effective. If sheetrock is used, make sure that is properly installed. If you are also using the safe to protect media files another valuable documents, it is a good idea to purchase a commercial grade fire safe to put inside the gun safe for double protection.
Research multiple brands before you buy. Unlike other products on the market where brand may not represent significant differences, there are significant differences among brands of gun safes. For instance, Browning offers a patent pending door mounted rifle rack. Fort Knox uses advanced methods for security and stabilization. Brown offers a custom wood interior shelf with commercial grade burglary protection. Compare differences between brands to determine which brand has features that best suite your needs, and then select the size and lock style you need.
Pay close attention to the specifications of the safe. Just because the unit is heavy and large, does not make it secure. Heavy gauge steel is less likely to be cut and drilled than light gauge steel. Some safes out there have thin walls that can be penetrated with a fire ax.
Research the safeguards against tampering for your safe. Quality safes will have extra armor or features that will prohibit drilling. Lower quality safes can be opened in a few minutes using battery-powered hand tools. All safes should have re-lockers to make sure the safe remains locked in the case of a burglary.
Don't expect to be able to find the right gun safe for your needs without shopping around. Make sure the safe meets all state regulations in terms of safety ratings. Have a professional install the safe, somewhere in your home that is out of plain sight. Train other adults in the home about how to use the safe, and educate children on an age-appropriate level about gun safety.
Still think owning a gun safe isn't worth the investment? A lot of times, you'll be storing a collection worth two to three times more than the safe itself – and quite possibly priceless, irreplaceable items. Divide the price of the safe, including shipping and installation charges by the number of years you expect to use the safe. Compare this dollar amount to the total value of the items stored in the safe. You'll quickly find the cost of the safe makes it an inexpensive insurance "policy" in and of itself.


It's interesting that the apostle Paul wrote that to the Christians in Rome, because there are some obvious similarities between the social climate of first-century Rome and that of much of the world of today.
Evil was rampant in Rome, and its pull was strong. The Roman Empire hadn't become the undisputed ruler of the Western world through compassion, kindness, or humility. Wealth was in the hands of a few, and they used it to control the rest. The rich and powerful lived extravagantly while the masses struggled to survive. Perversions and debauchery were practiced by some and ignored by others.
It is easy to feel overwhelmed by the evil in the world. Every day we hear about another horrible crime being committed. Meanwhile, the popular media searches for new and more horrific ways to portray violence, perversion, and all manner of evil. Whether it's a case of art imitating life or vice versa, life has lost its sanctity in the minds of many.
If a dish is dirty, being angry about the situation does nothing to fix it. Neither does trying to ignore it. The only solution is to expose that dirty dish to the power of a little soap and water.
If a room is dark, you can curse the darkness or whine over how unpleasant it is—or you can flip the light switch or open the curtains and let some light in.
It's the same with society's evils. We can get discouraged, angry, or depressed—"overcome by evil"—or we can be a force for good, even if only through personal example. Not every dirty dish will be cleaned, and not every darkened heart will be enlightened, but we can each do our part day by day, person by person, decision by decision.
Reblogged from Let Jesus Help You.
Romans 12:21 ESV – Do not be overcome by evil, but overcome evil with good.
Be intentional about what days are good for you to meet people. Make committments and stick to them. I always think it's a good idea to schedule meetings a week or two in advance so you don't do things last minute. That way you can be prepared. If you work for yourself, planning the use of your time wisely can be tricky. Make sure to pen in appointments and keep your diary close at all times!
Ideally, you want to stick to an agenda which you set. Get through the business first, then you can move onto the fun stuff if you still have time.
Time is precious. If you have lots to get through, make sure you structure your days well and stick to your goals. It's always a good idea to set out the time you have available when organising meetings. Literally say to yourself (and your client) how long you have for the meeting. 1 hour? 2 hours? Put a lid on how much time you can spend working on something with someone. If you don't do this, you may just run over time and disappoint yourself when you can't achieve your other goals for the day.
I sourced these ideas from Pinterest – check out my Healthy Eating Board here.
The Courtyard Hotel in Port Elizabeth has 91 suites and studios and it's located 8 minutes from the CBD and 10 minutes from the Airport. It offers stunning views of Algoa Bay + is a 5 minute walk from one of Port Elizabeth's premier attraction, The Boardwalk Hotel & Casino.
City Lodge Hotel Group has a beautiful thing going here. The Courtyard Hotel was pretty on the eye, well-run and inviting. The staff were all very capable of making me feel safe, organised and welcome. They truly aimed to give me the best service and insight into the city's tourist attractions for my convenience and pleasure. I had two dedicated receptionists – Pretty and Roniqu- who were cheerful and professional. A special mention must also be made to Matthews and Thabisa.
I found the room very clean and spacious. A large bathroom offering a bath and shower greeted me, as well as a self-catering kitchenette with coffee and tea making facilities. This was beyond my expectations for a standard room. I enjoyed the full DSTV bouquet and the balcony, overlooking Port Elizabeth's gorgeous ocean. A thrilling sunrise greeted me when I woke up from a cold South African winter's night – Thank goodness for the air conditioner to warm me up. I was also pleased with the cosy bed and the extra blanket.
The complimentary drinks and snacks at the fireplace in the hotel's lounge relaxed me after I arrived to check in following a busy day! Complimentary hot snacks and drinks – alcoholic and non-alcoholic – are available to guests every night between 5pm and 7pm. The sight of their large, well-furnished lobby and seating area was a sight for sore eyes. I loved the modern feel of the whole hotel. I particularly remember how sweet the air smelt when I caught sight of their clean, blue pool overlooking the salty PE sea.
Breakfast offered a delectable selection for guests. There was an extensive buffet for both Continental and full English options. Here's a tip: Try ordering a white omelette – the chef prepares it superbly.
If you like your Internet access, The Courtyard Hotel offers 60 minutes complimentary Wif-Fi daily. If you're a health fundi, they also have a gym! For business travel, make sure to use their conference room.
Again, the hotel offers a "kind-cierge" which organises day trips for you.
For an experience at sea, I recommend Jester Cruises. They offer 90 minute cruises through the bay at R150 on Saturdays and Sundays between 3pm and 4:30pm. On Wednesday and Saturday evenings, their braai cruises are R100 more per person and run from 6:30pm-10pm.
If you're anything like me, you would enjoy a true African Spa experience. I visited the Africology Spa – a 5 minute walk from the hotel. The hotel can organise a shuttle for you. This spa experience is definitely a must for the tourist if you need some pampering. I enjoyed a cleansing 35 minute "Majestic Spa Awakening" therapeutic massage. It treated my neck, shoulders and scalp. My therapist, Zola, is a true asset to her craft. Her massage ability is heaven-sent and her God-given ability will leave you asleep on the bed. Enjoy this for R400. During this month of July, they are offering a "Madiba" special, which includes an extra 20 minute foot massage. The spa also offers Hydrotherapy, Grooming Packages and Skin Wraps. For an added R150, enjoy a sauna, hydro-bath or steam therapy. End off your experience with a cup of mint tea in their relaxation room.
Three restaurants I love in this street are Charlie Superstar, For The Love of Wine and Salt.
Thank you City Lodge! And thank you to my photographer Kevin Ruth.
Thailand is one of the best places for South African travellers – airfare is affordable, night markets offer bargains and you can find good food in both fancy hotels and street-side stalls. Phuket is Thailand's largest island. You won't have trouble getting around! I spent New Year's Eve in Thailand once. It was fantastic because it took me totally out of my comfort zone which I loved.
Everywhere you turn there's something new to discover in Venice. For the first few hours after I arrived I couldn't believe I was really there. Known for its winding canals and ancient bridges, it's also pretty famous for its artisan crafted masks and costumes worn during the carnival, as well as for the Peggy Guggenheim Collection. One of the most memorable days for me was when we visited the magnificent Piazza San Marco, which Napoleon referred to as the "drawing room of Europe". I was in complete awe.
Social Media PR + Marketing for Gravel Road's "Retro Afrika Bioscope" at Waterfront Film Studios!
See the crew working hard on digitally remastering the films – read more about them here. See the gallery of behind-the-scenes here (Photos by Patrick Ryan, shot on location at the Waterfront Film Studios).


Crash and bash your way to victory in the TRANSFORMERS BOT SHOTS Battle Game! Will your OPTIMUS PRIME vehicle convert to robot mode and make you the winner? Find out when you crash your vehicles together! Crash and bash your way to BOT SHOTS victory!
Choose your OPTIMUS PRIME vehicle's power, then crash into battle with your opponent. Two ways to win when you crash your vehicles together! If your vehicle converts to robot mode when your opponent's doesn't, you're the winner! If both of your vehicles convert, the vehicle with the highest power wins! Blaster beats Fist, Fist beats Sword and Sword beats Blaster. If neither vehicle converts, crash them all over again!


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Language Inc
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Norwegian belongs to the northern branch of Germanic language along with Swedish, Danish, Icelandic and Faroese, and is particularly close to Swedish and Danish. Except for small communities of migrants, the language is not spoken outside Norway. When describing Norwegian, it is important to distinguish clearly between the written and the spoken language. In writing there are two official norms, Bokmål (literally 'Book Language') and Nynorsk (literally 'New Norwegian').
Bokmål - Nynorsk
The difference between Nynorsk and Bokmål is socio-linguistically important. At present Bokmål is dominating in all sectors of the Norwegian society as 85–90% of the population are writing in Bokmål. It is often associated with urban culture and an urban way of life and used in advertising, pop music, fashion, entertainment and young people's culture. It is the language of weekly magazines, big newspapers, the commercial world and the world of technology. On the other hand Nynorsk is used by about 10-15% of the population. It is often associated with traditional and national values and with regional or local culture. Writing Nynorsk might be a signal that your identity is more related to local values than to urban style, and that your local roots are of great importance to you. Nynorsk has a rather weak position in technology, economy and popular culture. Still, Norwegians are educated in both Bokmål and Nynorsk. The Norwegian broadcasting corporation (NRK) broadcasts in both Bokmål and Nynorsk, and all governmental agencies are required to support both written languages. Bokmål is used in 92% of all written publications whilst Nynorsk in 8%. A newer trend is to write in dialect for informal use. When writing an SMS, Facebook update or fridge note, younger people write the way they talk rather than using Bokmål or Nynorsk.
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Navigant's Cyber Risk and Information Security PracticeLearn More
Compass Cyber Solutions A Navigant Consulting Company
Cyber Security Assessments
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Robert (Bob) Olsen
Chief Executive Officer, Chief Information Security Officer
As the co-founder of Comprehensive Applied Security Solutions (COMPASS) and North Star Group, Mr. Olsen has more than 20 years of experience in operations, information technology, engineering, and finance. He is responsible for the daily operations and strategic direction of North Star Group and COMPASS.
Prior to co-founding COMPASS and North Star Group, Mr. Olsen served as vice president of program management and corporate services for Broadwing Communications (Austin, Texas). In this role, Mr. Olsen managed network infrastructure, corporate acquisition integration, product development, customer implementation, corporate quality, and strategic initiatives on a nationwide scale.
Mr. Olsen began his career in the 82nd Airborne Division, United States Army. Upon completing his obligation, he worked at Verizon Communications, achieving the level of regional engineering manager. Mr. Olsen went on to hold several senior-level positions with another organization, Corvis Corporation, in the areas of field operations, technical services, project management, quality assurance, information technology, and global education services.
Mr. Olsen graduated from Towson University (Towson, Maryland), with a bachelor of science degree in liberal arts. He later earned a master of science in information and telecommunication systems and a master of business administration from Johns Hopkins University (Baltimore, Maryland). Mr. Olsen is a Certified Information Systems Security Professional (CISSP), Certified Cloud Security Professional (CCSP), Certified HealthCare Information Security and Privacy Practitioner (HCISPP), Certified Information Security Manager (CISM), and an adjunct professor for cyber security at The Johns Hopkins University Carey Business School. Mr. Olsen also serves as a board member of the Dean's Advisory Council at The Johns Hopkins University Carey Business School. Furthermore, Mr. Olsen was named one of Maryland's Most Admired CEOs by the Daily Record.
Read blog posts by Bob Olsen >>
Mike Shveda
Mr. Shveda is a co-founder of Comprehensive Applied Security Solutions (COMPASS) and North Star Group and a seasoned professional with more than 30 years of experience in project and program management, operations, and engineering.
Prior to co-founding COMPASS and North Star Group, Mr. Shveda served as the director of program management for Broadwing Communications (Austin, Texas), where he led the development and implementation of project management processes—and increased the maturity level of the project management office (PMO) several stages. In this role, Mr. Shveda managed network infrastructure, product development, and strategic initiatives on a nationwide scale. Additionally, Mr. Shveda was responsible for a significant first, with the offering of both Layer 2 and Layer 3 Virtual Private Network (VPN) services on a new Converged Services Network based on Multiprotocol Label Switching (MPLS).
Mr. Shveda began his career with the Naval Air Systems Command (NAVAIR) and received successive promotions, culminating in his role as senior project engineer for both the Navy's F/A-18 Fighter Aircraft and Sparrow Missile System programs. Following his employment at NAVAIR, Mr. Shveda served as a program manager at the Federal Aviation Administration (FAA), and became a product team leader for multiple cross-functional teams and successfully delivered the following key communications programs: Voice Switching and Control System, Air/Ground Communications, NADIN II Packet Switching Network, Low Density Communications Links, and the Data Multiplexing Network. Following his tenure at the FAA, Mr. Shveda went on to hold several mid-level positions with Corvis Corporation in the areas of project and program management.
Mr. Shveda graduated with honors from the University of Tennessee (Knoxville, Tennessee) with a bachelor of science degree in aeronautical engineering. In addition, Mr. Shveda holds a master of science in technology of management from American University (Washington, DC), is certified as a Project Management Professional (PMP) by the Project Management Institute, and is a graduate of the Department of Defense (DOD) Defense Systems Management College.
Mr. Shveda is also the author of "Who's Going to Project Manage This?"
Read blog posts by Mike Shveda >>
Karen Wade
As Finance Manager, Karen Wade is responsible for the daily business operations to include all financial functions, DCAA compliance, government contract management, company policies & procedures, and facilities management. In addition, Karen closely coordinates with Human Resources Management, to assist in employee development, training, benefits, team building, employee engagement, payroll and performance management. Karen is an entrepreneurial leader with over 20 years of experience managing accounting and human resources functions for small to medium sized business environments.
Prior to joining COMPASS, Mrs. Wade was an Accounting & HR Manager and Business Consultant with technology companies to include Hewlett-Packard, Pointivity, ChannelCloud, and Dove Tree Canyon Software. Other experiences include license management and reporting for Minor League Baseball, Dr. Seuss and LRG among others.
Karen has a Bachelor degree in Business Administration and Management from the University of Puerto Rico and a Master of Business Administration and Finance from the Inter American University of Puerto Rico – Bayamon.
Steve Morrill
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"COMPASS is a trusted partner of Loyola Blakefield. Their assessment of our network infrastructure, policies, and procedures was very thorough. It was clear from the initial discovery meeting that the …"
COMPASS Joins Navigant Consulting
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Q: PubNub WebRTC demo working in same network but not over internet (even after established connection) I was going through this PubNub WebRTC demo. https://kevingleason.me/SimpleRTC/minivid.html
Which works fine within same network (same browser or different devices across same network). But I tried using it over internet, I am able to connect a call but can not see anything but a black screen. This is the source for same tutorial
https://github.com/pubnub/SimpleRTC
I have gone through many such application, such as AndroidRTC
and I face same problem (black screen after connection over internet). I am unable to figure out why, any help is appreciated.

A: You need some sort of signaling mechanism (PubNub, Firebase, or your own software [nodejs seems the preferred choice these days]) to get the webRTC API communicating P2P on your local network. To get webRTC to work from one network to another you need a STUN server/service. Google provides free stun servers (stun:stun.l.google.com:19302). To get webRTC to traverse strict firewall settings and complicated networks you need a TURN server/service like xirsys.com.
This article covers it all ...
http://www.html5rocks.com/en/tutorials/webrtc/infrastructure/

redpajama_set_name: RedPajamaStackExchange