- freely available
Sustainability 2019, 11(4), 1002; https://doi.org/10.3390/su11041002
- More efficient and sustainable cities.
- More efficient and sustainable transport and mobility.
- Improvement in the environmental quality of the city.
2. Related Work
3. New Contribution
4. Definition of the Study
4.1. Sensor Description
- The sensor used  in this study has been developed by the University of Valencia in collaboration with the company UVAX Technology in Action. Its main features are:
- Two BT interfaces
- Two WIFI interfaces
- Two block of antennas, each block composed of two antennas (90º horizontal beam width, 30º vertical beam width). These work at 2.4 GHz and 12 dBi.
- Ethernet 100Mbps.
- GPS + Glonass
- Cortex A-8 1 GHz processor
- 1 Gb RAM
- 16 Gb ROM (it allows storage of up to 6 months of detections)
4.2. Definition of the Itineraries
- Route 1–3: La Paz - Comedias with San Vicente - Maria Cristina, distance 500 m.
- Route 3–4: San Vicente - Maria Cristina with Torres de Quart, distance 800 m.
- Route 2–3: Conde Trénor with San Vicente - Maria Cristina, distance 1000 m.
- Route 2–4: Conde Trénor with Torres de Quart, distance 1100 m.
4.3. Location of Sensors
- Location 1: Paz - Comedias, entry sensor, allows the detection of the vehicles that enter the historic center and follow route 1–3.
- Location 2: Conde Trénor, entry sensor, allows the detection of the vehicles that enter the historic center and follow route 2–3 or 2–4.
- Location 3: San Vicente between Plaza de la Reina and Plaza del Ayuntamiento, exit sensor, allows the detection of the vehicles that leave the historic center in route 1–3 or 2–3.
- Location 4: Quart-Murillo, exit sensor, allows the detection of the vehicles that leave the historic center in route 2–4 or 3–4.
- Location 5: Guillem de Castro - Lepanto, intermediate sensor, detects vehicles that have been registered at the entry of Conde de Trénor, but have not entered the city center, they have followed the internal ring road. These vehicles were detected by the rear lobe of the antenna. Devices that followed itinerary 2–5–4 were disregarded in the current study because they do not use the historic center.
4.4. Description of the Study
- Establishing the location of the sensors, trying to minimize the power cabling, being careful that there would be no obstacles between the sensor and the vehicles and making sure it covered a wide area of the street to improve the detection of the devices.
- Installation and configuration of the equipment.
- Controlled tests with known BT devices to determine the classification algorithm for pedestrians and motor vehicles.
- Removal of equipment and collection of sensor data.
- Filtering the data of each sensor and creating the detection intervals. All detections of the same device are grouped in each sensor while it remains in the detection area. The intervals created coincide with the times the device passes through the detection area.
- Creation of trips between sensors.
- Application of the classification algorithm for the trips to separate vehicles from pedestrians.
- Calculation of the travel time and classification of the trips according to the criteria established in the study. Trips that do not stop on the route and use the itinerary to shorten the route through the city and trips that have as origin or destination some point of the historic center of Valencia
- Presentation of the results and conclusions.
5. Results of the Study
5.1. Classification Algorithm for Vehicles and Pedestrians
5.2. Detection Rate
5.3. Travel Time
- (0): Initial travel time between sensors i - j.
- : Travel time associated with the free flow speed between sensors i - j.
- (p): Average travel time at instant p between sensors i - j.
- (p): Calculated travel time of trip k between sensors i - j at temporal instant p.
- sup: Constant, in this study it takes value 2, sets the travel time value from which it is considered that the vehicle has made arrangements between the sensors (vehicle circulates two times slower than the average travel time).
- inf: Constant, in this study it takes value 0.3, it sets the travel time value from which a lower value would be a non-real data (vehicle circulates three times faster than the average travel time).
- β: Constant value that allows setting the value of taking into account the travel time value of the new valid trip registered. Typical values between 0.03 and 0.05
- : Trips classified as valid, value of the O/D matrix in the route, in the temporal interval p.
5.4. Percentages of Travel Distribution
Conflicts of Interest
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|Transport Mode||Mean of Number of Detections||Typical Deviation|
|MAC||Device||Number of Trips||Classified as Vehicle||Classified as Pedestrian|
|Date||Daily Traffic |
|Filtered Detections||Total Detections||Filtered Detections |
|Total Detections |
|Route||Number of Trips||Passing||Non Passing|
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