The purpose of this field trial is to enable citizens and visitors to get access to enhanced urban mobility experience and to leverage city transportation resources efficiently.
In this sense, vast amount of information generated in the city associated to the use of public transportation, joined to user events and preferences and to other type of data to be considered such as environmental parameters, is accordingly processed in order to provide user with recommendations for reaching a certain destination.
Information about public buses fleet, as their schedule, position, speed, estimated time to arrive, occupancy, expected travel time, as well as information related with other means of transport such bikes, taxis, trains, will be leveraged and combined on a single application. All these data are also combined with information regarding traffic parameters, such as speed, occupancy degree, indoor and outdoor parkings, as well as with environmental indicators such as CO, NO2, O3, noise; all of them provided by the static deployed devices at main areas of the city or on mobile devices such as the public vehicles or the citizen smart phones. Additionally, users’ preferences and events generated by them are also handled by this application.
With the gathered information, it is possible to generate real time alerts and events about urban traffic in the city, which could be highlighted in a map with its associated location; as well as to show the user the best choices for accessing to the corresponding destination. All this information and functionalities are integrated in a mobile application, as shown in the Figure 2 below.
In the figure, they can be observed three different routes requested by the user, where the origin is given by the green marker and the target is represented by the red one, and it is offered information about five different means of transport are considered: bike, foot, car, taxi and bus. In the left side, it is highlighted the route by bike, thus indicating the number of bikes available in the closest bike stop to the origin point. In the figure in the middle, it is selected the route by car thus offering information about the parking lots available in the nearby of the destination point. Finally, last one shows the taxi option, thus indicating the number of taxis available in the closest stop to the origin point. Additionally to this, independently from the selected option, in the bottom part of the screen they are included some specific route details (distance, time to go, …), and as shown in left and right figure also information from some of the environmental sensors closest to destination and origin, is also shown.
This application gathers information coming from a big quantity of information sources, such as legacy devices (traffic information), IoT resources (environmental, parking and in-vehicle sensors), users’ events/preferences (specific mean of transport), city provided information (number of bikes/taxis per stop) , being able to gather and processing it in an efficient way, leveraged by ClouT project architecture, thus providing important information to the users on the different options to get to the corresponding destination and mobility related events in the city.
It is a sunny day, Pedro is in his home and decides to go to the beach with some friends. In order to meet with them and plan the best way to get to the beach, Pedro accesses to the Traffic Mobility management application in order to try to select the best way to get to the beach observing that:
- In the way to the beach (passing by the house of his friends), there is some road work shown in the map of the application, indicating that the traffic is slow in this road.
- In the bike stop near his home, two bicycles are available, but for avoiding the high levels of noise (associated to the road work), indicated by fixed installed sensors and measurements retrieved by mobile phones, he would prefer to avoid this street.
- Looking at the outdoor/indoor parkings located near the beach, it seems that it will be complicated to park as rate of rotating parking lots is quite low.
- The next bus passing by closest bus stop is in 10 minutes and the following one will be in 40 minutes.
Considering the different information offered by the application, Pedro finally chooses the option of taking the bus as the most comfortable one considering the road work and the difficulty for parking the car.
Integration with ClouT platform
The picture below shows the general architecture of the “Traffic mobility management” field trial application following the ClouT three layers representation:
According to Figure 3, two main modules have been implemented for processing information and showing it accordingly to the user:
- Mobility transport application: Located within CSaaS layer, it presents a map of the city and a menu for user’s interaction. This menu is composed of different text boxes for including source and destination coordinates (can be also directly pinned in the map). Once defined source and destination, application uses open source maps and route calculation servers for providing route estimation per each mean of transport, thus showing specific information of the specific route in a dialogue box.
- Mobility management module: Integrated in the CPaaS layer, it is in charge of accessing the ClouT storage, asking for information related to environmental, transport, and traffic associated to the calculated route.
The rest of modules in the figure are common entities within Clout architecture, intended for taking information from deployed IoT devices (SmartSantander GW and Public IoT API modules), process and aggregate the information retrieved (Service Proxy/Manager and Service Aggregator) according to a corresponding service (Service Storer) as mobility management in this case, and finally store information in the ClouT storage platform.
Improve users’ perception on managing the traffic and public transport issues, thus offering real time information and suggesting best available options for reaching a specific destination.
Apart from the route estimation directly related with the mean of transport selected, users will have also the possibility of knowing additional information (environmental, noise) associated with the selected route, provided by the different IoT devices deployed in the city.