7. Results

In (Eq. (1)) one maximizes the ranking generated by the selected POIs. According to (Eq. (2)), a PoI must be visited after the current location. Constraints (Eq. (3)) enforce for the tourist to come back to the current location after the visit of the last PoI. Constraints (Eq. (4)) guarantee that a tourists arriving at any PoI must also leave from that PoI. Constrains (Eq. (5)) link decisions variables on POIs selections and movement between nodes. Constraints (Eq. (6)) are the subtour elimination constraints of Miller, Tucker, and Zemlin. Constraints (Eq. (7)) enforce that the overall time spent to move between nodes and visit POIs is lower that the planned time interval. Finally, (Eq. (8)), (Eq. (9)), and (Eq. (10)) are the domain of decision variables.

It is worth noting that one does not have to visit all nodes of the N, unless a large value of d is considered. Moreover, the direct graph makes very easy to model the case in which starting

Consider the subset N of nodes selected in the previous model. These nodes may not be visited in an effective order, as this model does not aim to minimize the costs of movement between nodes. To correct this drawback, we consider a second model, in which a formulation of the Traveling Salesman Problem (TSP) is presented. The solution of the TSP determined the so-

The TSP can be described as the following graph theoretic problem. Let G N; A � � be a direct and complete graph, where A the set of arcs connecting nodes of set N. The following decision

> X j ∈ N

In (Eq. (11)) one maximizes the ranking generated by the selected POIs. According to (Eq. (12)) and (Eq. (13)), a node must be visited before and after the current one, respectively. Constraints

ti,j � Xi,j (11)

Xi,j ¼ 1 ∀i∈ N (12)

Xj,i ¼ 1 ∀i∈ N (13)

� � <sup>&</sup>gt;<sup>¼</sup> <sup>0</sup> <sup>∀</sup>ð Þ <sup>i</sup>; <sup>j</sup> <sup>∈</sup> <sup>A</sup> (14)

Xi,j ∈f g 0; 1 ∀ð Þ i; j ∈ A (15)

Ui ∈f g 0…N ∀i∈ N (16)

• Xi,j ∈f g 0; 1 is equal to 1 if the tourist moves along arc ð Þ i; j ∈ A, 0 otherwise;

MinX i ∈ N

X <sup>j</sup> <sup>∈</sup> N, <sup>j</sup>6¼<sup>i</sup>

X <sup>j</sup> <sup>∈</sup> N, <sup>j</sup>6¼<sup>i</sup>

Uj � Ui � 1 þ M � 1 � Xi,j

and arrival points are different.

44 Assistive Technologies in Smart Cities

called optimized itineraries mentioned throughout this paper.

• Ui <sup>∈</sup> <sup>0</sup>;…; <sup>j</sup>N<sup>j</sup> � � is the position of POI <sup>i</sup> <sup>∈</sup> <sup>N</sup> in the current trip.

The problem can be formulated as follows:

6.2. Second model

variables are defined:

In this section we show the viability of the proposed tools to support the mobility of physically disabled tourists or elder persons. We also analyze the case of able-bodied tourists for the sake of comparison. The difference between the two cases is shown by increasing travel times along uphill and downhill routes for disabled tourists as opposed to able-bodied ones. The experimentation is carried out in the city of Cagliari, where many tourists disembark from cruise ships at the harbor. They typically aim to visit the oldest part of Cagliari, which is known as the Castello. It clings to the slopes of a hill that rises steeply from the harbor. Therefore, in this case study it is of particular relevance to distinguish between the waking times of disabled and able-bodies tourists, in order to properly plan which subset of PoI should be visited, as well as the order of the visit.

Four classes of PoIs are considered, which correspond to different profiles of tourists interested in museums, monuments, gardens or shops. We took their location and their altitude from the open data platform and we derived the average slope of the streets connecting PoIs. The average travel time per unitary distance was calibrated by a sample of tourists with similar disabilities over a set of streets with different slopes. Since the distance between all PoIs is known, we easily derived the travel times among them.

All the PoIs are ranked with a value ranging from 1, less attractive, to 5, most attractive. A subset of PoIs is considered for each class by a score threshold, which specifies the PoIs the tourist wants to visit. For example, if it taken on value 2, we consider all PoIs with a score bigger than or equal to 2. We initially set the score threshold to 3 and relax the constrain on (7) and compute the itineraries for each class of PoIs. In Figure 5 the time to visit all PoIs is reported for all class of PoIs in four cases:

Figure 5. Minimum time for optimized itineraries with threshold = 3.


As expected, it takes longer to make closed itineraries than open ones and the overall visiting time for disabled tourists is larger than that of able-bodied ones. Next, we reintroduce constrain (Eq. (7)) and plan itineraries according to settings of the time limit d and score threshold. More precisely:


Figure 6. Optimized itineraries with maximum time 120 min and threshold = 3.

• In the results of Figure 8, d is set to 120 min and the score threshold to 2; • In the results of Figure 9, d is set to 120 min and the score threshold to 4;

Figure 9. Optimized itineraries with maximum time 120 min and threshold = 4.

Figure 8. Optimized itineraries with maximum time 120 min and threshold = 2.

persons.

8. Conclusions and future work

The obtained results show that the proposed tools can be customized to return a subset of PoIs for physically disabled tourists as opposed to the set of routes determined for able-bodied

Using IoT for Accessible Tourism in Smart Cities http://dx.doi.org/10.5772/intechopen.77057 47

Within the framework of Smart Cities, Accessible Tourism and Internet-of-Things (IoT), this paper analyses the key requirements for IoT applications in a Smart City context, the

Figure 7. Optimized itineraries with maximum time 240 min and threshold = 3.

Figure 8. Optimized itineraries with maximum time 120 min and threshold = 2.

• Open itineraries of disabled tourists from a given GPS location to the port (blue rhombus); • Closed itineraries of disabled tourists leaving and returning back to the port (red square); • Open itineraries of able-bodied tourists from a given GPS location to the port (green triangle); • Closed itineraries of able-bodied tourists leaving and returning back to the port (gray cross). As expected, it takes longer to make closed itineraries than open ones and the overall visiting time for disabled tourists is larger than that of able-bodied ones. Next, we reintroduce constrain (Eq. (7)) and plan itineraries according to settings of the time limit d and score threshold.

• In the results of Figure 6, d is set to 120 min and the score threshold to 3; • In the results of Figure 7, d is set to 240 min and the score threshold to 3;

Figure 6. Optimized itineraries with maximum time 120 min and threshold = 3.

Figure 7. Optimized itineraries with maximum time 240 min and threshold = 3.

More precisely:

46 Assistive Technologies in Smart Cities

Figure 9. Optimized itineraries with maximum time 120 min and threshold = 4.


The obtained results show that the proposed tools can be customized to return a subset of PoIs for physically disabled tourists as opposed to the set of routes determined for able-bodied persons.

#### 8. Conclusions and future work

Within the framework of Smart Cities, Accessible Tourism and Internet-of-Things (IoT), this paper analyses the key requirements for IoT applications in a Smart City context, the state-of-the-art for the use of IoT for Accessible Tourism and presents an IoT architecture for the specific Smart City scenario dedicated to the sustainable management of the tourist flow in the urban environment of Cagliari. Based on the presented IoT architecture, a Tour Planner Application with features for accessible tourism is presented, together with the mathematical optimization model used for generating a specific tour including a subset of PoIs. The proposed application is tailored for persons with physical impairments. The results of the initial tests are presented and first conclusions are drawn. The obtained results showed that the proposed algorithm can be customized to return a subset of PoIs for disabled tourists as opposed to the set of routes determined for able-bodied tourists. The future work will be focused on refining the used algorithm by taking into considerations new accessibility constraints and also other types of input, such as for example live accessibility data from public transportation.

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final.pdf
