Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case
- To explore how to translate the knowledge of the field of composite indicators to the port-city interface.
- To define how to build solid indicators with that purpose, focusing on their calculation and prediction to represent real-world phenomena in the context of a Smart Port-City
- To analyse the various technological options to construct an architecture leveraging IoT techniques covering the indicator implementation.
- To propose an IoT-based software framework accompanied with a methodology for its co-design and deployment on a real use-case.
- To effectively conduct a small scale experiment consisting of implementing a composite indicator use-case reflecting the traffic congestion in the interface between the port and the city of Thessaloniki (Greece).
2. Related Work
2.1. Open Data in Smart Ports and Smart Cities
2.2. Interoperability at Middleware Level of Heterogeneous Data in a City
2.3. Top-Layer Services for Smart Port-Cities Based on IoT
2.4. Real-Time Composite Indicators in Smart Port-Cities
2.5. Machine Learning for Traffic Congestion Forecasting
3. Materials and Methods
3.1.1. Data Broker Module
3.1.2. Data Storage Module
3.1.3. Orchestration Module
3.1.4. Visualization Module
3.1.5. Dual Computing Approach
- To retrieve the data from the original source: the framework has been designed to accept a two-fold connection mode: (a) the agent actively queries the data source origin following a periodic pattern. This option will apply whenever the data is behind a reachable API; or (b) the agent includes an embedded data broker so that the active origin can publish on it. This case is usually present in the cases where built-in IoT stations or smart sensors are used.
- To process the data and convert it to KPIs: this development will be different for each data source, becoming the most craftsmanship development when a composite indicator tool wishes to be deployed using our framework. It might range from a simple format conversion to a complex data relation, combination, and construction. Additionally, each NGSI agent may have different number of inputs and outputs. Despite the fact that the usual case (see Section 3.4.1) is to realise a 1:1 relation, the framework has been prepared to accept 1:N, N:1 and N:N setups as well.
- To update the entity in the Data Broker: The main goal of the agent is to make data reach the information layer of the IoT stack of our framework. Hence, the KPIs obtained are submitted to the Data Broker (ORION) via a PUT HTTP—Hyper Text Transfer Protocol—message in order to update the “KPI entity”. The format selected has been to extend one of the FIWARE Data Models: KeyPerformanceIndicator . More details can be found in Appendix A.
- To configure the CI inputs, the structure of the tree and the associated parameters, such as aggregation methods, normalization methods and weighting values through a developed visual interface. See Section 3.4.4. to discover the utilization of this component.
- According to the parameter set in (1), including the scheduling, the periodic calculation of the index. First, the KPI values are properly normalised (crucial step on a CI procedure). Then, starting by the leaf node, a cascading algorithm including aggregation, weighting and combination leads towards a final index value. Technologically, this component has been developed as a dockerised (packaged in a Docker container ) standalone Java application.
- To configure the predictive component of the framework by the user, specifying batch sizes, periodicities and model to be used for the inference of KPIs.
- According to (3), the training module groups the KPIs stored, fits again the pre-trained model selected, updates it, and makes it ready to be used. Technically, this module has been developed as a dockerised Python script.
- To apply the prediction over predicted KPIs. This module has also been developed as dockerised Python script.
- To visualise the results of both real-time calculation of the composite index (2) and the observation of the predicted evolution of the composite indicator via a specific dynamic graph (5).
3.2. Proposed Use Case
3.3.1. Traffic at the Gates of the Port Using Radio-Frequency Identification—RFID—Sensors
3.3.2. Traffic at the City Provided as Open Data
3.3.3. Vessels Berthed in the Port
3.4. Methodology of the Experiment
- Definition and development of the NGSI agents: developed by technical experts after gaining information of the data available (see Section 3.2).
- Definition of the composite index (TCI) by stakeholders using the visual interfaces developed in the framework.
- Setup and running of the predictive component, from user parameters to actual software running.
- Configuration, implementation, and integration of all the pieces together to have a real-time composite index calculation.
3.4.1. Definition and Development of NGSI Agents for the TCI
3.4.2. Definition of the Traffic Congestion Index
- Grouping of data into KPIs: Following the state of the art analysed in the projects PIXEL and CITYkeys, the authors decided to group the data for feeding KPIs by common origin and meaning . This drove the design to couple the traffic on one side (gates of the port and city), vessel information on other side and weather information on a third and final string. The leaf nodes (KPIs) were also individually separated by isolated pieces of information (Gate 16 of the port, Gate 10A of the port, vessel count, temperature, wind speed and precipitation intensity). This led to a three-levels composition, being the KPIs the leaf nodes, three subindices and the TCI as the root node, resulting in a 7:3:1 matrix.
- Exploratory Data Analysis (EDA) of available historical data: Authors carried out a thorough EDA of the historic of data (see Section 3.3). A summary of that EDA is attached in Appendix B. The main aim of this EDA was to discover how the data performs through time, noticing seasonality, and, mainly, to find the correlation between the different data with the main reference source: the traffic at the gates of the port. The results of this correlation were the following:
- Weighting: The last-but-one configuration for the CI was to select the weighting method and values to give to each node. This is a crucial action that has been widely studied in the composition of CIs . There is not a universal weighting method and it must be analysed case by case, introducing a challenging choice . The most used method is equal weighting, followed by analytical methods (regression analysis, benefit-of-doubt, principal component analysis), with opinion-based methods only used marginally. The choice depends on the nature of data and indicator items: (i) equal weighting tends to be used when no historical data is known, (ii) analytical methods are case-to-case analysed depending on the items and (iii) opinion-based methods are mostly used in social sciences (cases where the target indicator is highly subjective). In this experiment, the authors opted for an analytical method driven by the analysis of historic data available. The specific scheme selected was to base the weighting values on the correlation of all items with the main reference item. For establishing those values, g the authors came to map KPI-weight, sub-index-weight, which brought to solve three equations systems (see numbers and structure of the points above).
- Aggregation method: According to the literature, there are three widely used aggregation methods : additive aggregation, geometric aggregation and non-compensatory aggregation, being the first one the most popular by far. Additive aggregation provides transparency and allows a simple understanding of the results, being much dependent on the synergies between items. Geometric aggregation also provides a good understanding of results but requires uniformity in measurement units and scales, being very dependent of synergies as well. Non-compensatory methods are fit for cases where the indicator is going to be deployed in several instances (e.g., ports, countries) and those aim to be compared and ranked. Non-compensatory methods are the most computationally expensive of the three. For this experiment, the authors considered that the relevance of all indicators was not equal, being the traffic at the gates the reference item. Noticing the previous, the choice was to select additive aggregation. However, the framework developed in this paper has been designed to allow the selection of any of them for future uses.
- Normalization method: The items to build the indicator must be comprehended in the same scale in order to be aggregated. Thus, a normalization step was included in this computation. A lot of methods for normalization are available, and to be able to finally select one as definitive several robustness test must be done. The objective of the work in this paper was to demonstrate the use of these calculations using a specific IoT architecture rather than going deep into normalisation arguments. Scale method was discarded due to magnitude variation, as well as the ranking due to its only execution in Thessaloniki. Z-score method and Min-Max were the main candidates and, considering that in this experiment the authors had valuable historic data for more than 18 months, the Min-Max option was selected.
3.4.3. Predictive Component: Training and Validation
- To convert and clean the historic CSVs into the accepted data format for training the Prophet model. Here, the attributes had to be adapted and seven different models were created. The framework and methodology designed (see Section 3.1) established that all KPIs must be predicted, and then the CI is calculated after those values. For that reason, one model per each KPI was trained and used. This was developed by the authors using Jupyter Notebooks . The procedure was studied to be replicated in an automated way for (2).
- To create Python scripts that gather and group the KPIs data from the data storage and convert, clean, and adapt the information for re-training the seven models. In this experiment, three re-trainings took place, according to the framework usability evaluation depicted at the beginning of Section 4. The outcomes were new models that have used more historic data to be trained, therefore more accurate and usable. For storing and retrieving those models (binary files), the Python built-in library pickle has been used.
- To apply the models. As commented, the forecasting horizon was set to 1 day with a 60 min periodicity. This way, each morning, the framework runs the inference over the trained models. Prophet models do not need input data; therefore, a prediction is requested (automated through a Python script) with that periodicity and horizon. The outcome (future timestamps with predicted TCI values) are used to be represented for the user via the UI component.
3.4.4. Component Integration and Deployment
3.4.5. User Configuration
0.2 ≤ TCI < 0.46, Medium congestion
0.46 ≤ TCI < 1, High congestion
- composite indicator makes global sense and represents the reality,
- predictions are valid and realistic, so that the model can be trusted, and
- the interface usability makes it easy to interpret the information to build Smart Port-City policies upon.
6. Conclusions and Future Research Lines
Conflicts of Interest
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|Requirement||Type 1||Coverage by Module|
|Short- and long-term storage of the data||Technical||Data storage|
|Automatic scheduled execution||Technical||Orchestrator|
|Flexibility for filtering, selecting the data||Technical||Data storage|
|Semantic interoperability and common syntax||Technical||Data Broker, agents|
|Agile integration and deployment||Technical||Containerisation|
|To be able to add new data sources||Functional||Data Broker, agents|
|To train a predictive model and use it for inferring CI||Functional||Training, inference|
|To setup the weights and methods to calculate the CI||Functional||CI, orchestrator, UI|
|To visualise the current value of the indicator (real-time)||Usability||UI – User interface|
|To visualise the evolution of the indicator during a day||Usability||UI|
|To visualise the predicted evolution of the indicator||Usability||UI, training, inference|
|To make the framework as configurable as possible||Usability||UI|
|Event to Model||Actor Intervening||Relevant Data||Further Usage of Index|
|Traffic congestion at the interface of the port with the city—TCI||Port Authority||- Traffic at the gates of the port|
- Vessels berthed at the port
|Internal process- and relationship-centric practices, leading to less traffic and pollution.|
|City Municipality||- Traffic in the city|
- Weather in the city
|Monitoring and auditing.|
|Public/citizens||Knowledge and port acceptance.|
|Data Source||Technology Used||Relevant Parameters||Refresh Frequency of Pre-Processed Data||Units||History Available|
|Gates’ traffic||RFID||Vehicles||Per hour||Vehicles/hour||April 2018|
|Weather||Meteo station||Temp, wind speed, precipitation intensity||Per day||°C, kmh, mm||September 2018|
|City traffic||GPS||Average speed||Per hour||Kmh/vehicle||April 2018|
|Vessels in port||Vessel calls||Time ranges||Per hour||# of vessels||April 2018|
|Gates traffic—Historic||Active over static file||CSV||Pre-processing explained in Section 3.3.||-||Kpi-traffic-gate-10A/16|
|Gates traffic—real-time||Active over dynamic source||REST API||Filtering, grouping and JSON conversion.||60′||Kpi-traffic-gate-10A/16|
|City traffic||Active over external website||CSV||Pre-processing explained in Section 3.3||60′||Kpi-traffic-city|
|Weather||Active over external website||CSV||Pre-processing explained in Section 3.3||24 h||Kpi-weather-temperature|
|Vessel count—historic||Active over static file||CSV||Pre-processing explained in Section 3.3||-||Kpi-vessel-count|
|Vessel count—real-time||Active over dynamic source||REST API||Filtering, grouping and JSON conversion.||60′||Kpi-vessel-count|
|Average speed in nearby streets||−19.2%||As the cars move slower (less speed), the more traffic at the gates is experienced, being the most statistically relevant factor influencing the congestion|
|Number of vessels berthed||+11.7%||More vessels at the berth, more traffic at the gates. Although the correlation is not high, this % is significant.|
|Temperature||−7.7%||The colder, the more traffic, which under a logical point of view: summer-less traffic. Not much statistical impact.|
|Wind speed||−8.6%||The winder, the less traffic, but with no relevant relation to be conclusive.|
|Precipitation intensity||+3.7%||Very loosely coupled, almost no statistical correlation.|
|CPU||4 CPUs x Intel® Xeon® CPU E3-1220 v5 @ 3.00 GHz|
|Storage Memory||HDD 100 GB|
|RAM Memory||16.05 GB|
|Cluster||FUJITSU PRIMERGY TX1330 M2|
|Server OS||Ubuntu Server 18.04.4 LTS|
|Node.js + npm||12.18.1 LTS + 6.14.5|
|Python + pip||3.8.3 + 20.1.1|
|Docker + Compose||18.09.7 + 1.17.1|
|Development IDEs||Eclipse IDE for Enterprise Java Developers (v. 2020-06)|
Visual Studio Code (version 1.46)
|Sc.#||Date||TCI Value||Explanation and Evolution|
|1.1||19 March 2019|
|0.48240252||Peak is observed at the hour of the scenario execution, which is aligned with the EDA and the expected situation. The curve is a bit more flattened than logically expected, but downwards timing seems proper.|
|1.2||21 March 2019|
|0.46978970||The hour of the scenario is exactly experiencing the start of congestion dawn, where the peak was reach just before 3 p.m. The prediction curve looks proper.|
|1.3||23 March 2019|
|0.31322503||Scenario 1.3 can be trusted as well as the prediction for the central hours (9 to 15) experiences usual up and downs and it is kept between mid-congestion margins.|
|2.1||13 August 2019|
|0.43149143||As a usual Tuesday, levels of congestion remain constant high levels during the labor days. Curve reflects with high accuracy the usual picture in vessel-operations busy months.|
|2.2||15 August 2019|
|0.20111357||Scenario 2.2 shows unusual representation: despite being a Thursday, the traffic congestion is experienced and predicted between 0.2 and 0.3. However, this must be considered nothing but a good functioning of the framework and the prediction, as the 15th August is national holy day in Greece.|
|2.3||17 August 2019|
|0.35446125||Scenario 2.3 follows the same rationale than 1.3, therefore it is valid. The only addition is that more traffic congestion (in general), is perceived and forecasted. This makes sense as 17th August is summer period and, normally, more traffic is experienced in the city on Saturdays at central hours.|
|3.1||14 January 2020|
|0.44768116||Same exact observation, thus rationale than 2.1.|
|3.2||16 January 2020|
|0.50527066||As in 1.2, hour of experiment coincides with congestion dawn. Curve looks legit.|
|3.3||18 January 2020|
|0.21129410||Scenario 3.3 registered one of the lowest TCIs, both at the measurement hour and in the real-time previous values and the forecasted (max. 0.3). This makes sense as winter’s Saturdays are less congested (in general) than the rest of the days. City’s weekend tourist life is not as vibrant as at summer.|
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Lacalle, I.; Belsa, A.; Vaño, R.; Palau, C.E. Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case. Sensors 2020, 20, 4131. https://doi.org/10.3390/s20154131
Lacalle I, Belsa A, Vaño R, Palau CE. Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case. Sensors. 2020; 20(15):4131. https://doi.org/10.3390/s20154131Chicago/Turabian Style
Lacalle, Ignacio, Andreu Belsa, Rafael Vaño, and Carlos E. Palau. 2020. "Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case" Sensors 20, no. 15: 4131. https://doi.org/10.3390/s20154131