Data-Driven Urban Planning

Unleashing Insights through Data-Driven Spatial Analysis

Towards traffic analysis, image credits Noumena

Urban planning is undergoing a transformation, driven by the ever-expanding capabilities of data-driven technologies. In Barcelona, where the Superblock model has sparked intense debates and discussions, the integration of data-driven approaches is reshaping the way policymakers and stakeholders make critical decisions. In this article, we explore how data-driven tools are being adopted today in urban contexts, offering a pathway to more effective and sustainable urban transformations.

To establish effective intervention criteria for Superblocks, gathering data on public spaces, pedestrian occupancy, traffic, and mobility has become essential. The municipality of Barcelona has already implemented various data collection solutions [1], but with emerging technologies, new data-driven tools have emerged, offering diverse approaches to uncover urban dependencies, identify patterns, and reveal implications through data-based observation and analysis.

The Integration of Data-Driven Tools

The integration of data-driven tools with computational methods provides a powerful means to gain a comprehensive understanding of urban dynamics. Physical data gathered from urban environments plays a pivotal role in informing urban intervention decisions.

Data serves as a valuable instrument for calibrating, comparing, and validating parameters derived from the usage of physical spaces, triggering the implementation of tools able to describe traffic and mobility [2], facilitate citizen participation [3], or even adjust digital simulations [4].
Especially, in regards to mobility, a crucial topic for the Superblocks model in Barcelona, harnessing this wealth of traffic data paves the way for precise travel time measurements to be obtained, empowering decision-makers with accurate insights.

The Role of Intrusive Sensors

In Barcelona, the extensive use of intrusive sensors, such as inductive loop detectors, has played a pivotal role in urban planning.

Inductive loop detectors (ILD), as described by Singh, Vanajakashi, and Tangirala, were initially introduced at the beginning of the 1960s and have become one of the most popular traffic detectors due to their widely extended technology and simple mode of operation [5]. These detectors are available in various configurations, dimensions, and shapes to suit different application contexts, whether installed underneath or on top of the road surface. Typically, a wire is used to form several loops, which, while not affecting the sensitivity of the signal provided, offers an advantage in terms of signal reception.

ILDs operate based on the principle of mutual inductance, detecting vehicles by measuring the change in inductance caused by their presence on top of the sensor. This change in voltage, known as the vehicle signature, serves as the raw output from the detector system.

RADAR, GPS, and RFIDs

In the framework of urban data collection, it is possible to identify another typology of a non-intrusive set of sensor technology. Non-intrusive sensors are kept over the roads or to the side of the roads. This category includes ultrasonic, infrared, microwave radar, and acoustic sensors.

Ultrasonic sensors release sound energy at frequencies ranging from 25 to 50 kHz, allowing them to recognize both pedestrians and automobiles within their transmitter’s range. They are known for their cost-effectiveness and straightforward hardware. However, it’s important to note that ultrasonic sensors have a fundamental disadvantage; they can be influenced by external factors such as environmental sounds and noise [6].

Radio Detection and Ranging (RADAR) works on the principle of Doppler’s effect as the moving body approaches nearer, frequency increases; and as it goes away, the frequency decreases. The radar gun targets directly towards any vehicle and the device displays the speed on it [7].

GPS technology revolutionized navigation and location-based services. The launch of the low-Earth orbit satellite, Sputnik 1, by the USSR (Soviet Union) on October 4, 1957 may possibly mark the beginning of Global Positioning System (GPS) technology. Scientists studying the low-Earth orbit of this early satellite realized they could track the satellite by the relative strength of its radio signal [8]. In transportation, GPS enables vehicle tracking, navigation systems, and real-time traffic updates, reducing travel time and improving efficiency.

RFID technology plays a crucial role in object identification and tracking. RFID tags, whether passive or active, enhance location accuracy and overall effectiveness. Equipping vehicles with RFID readers and strategically arranging RFID tags can significantly improve location tracking.

Limitations

The technologies integrated to this date offer a reduced range of solutions. Radars and traffic loops have been implemented in several streets to monitor traffic, offering nevertheless unclassified representation of mobility dynamics, generating data assets ignoring street morphologies, traffic lanes and classifications of vehicles, therefore providing limited data in terms of an actual representation of more complex mobility estimations, carbon emissions and deeper traffic analysis of mobility infrastructure, as you can see in the open data platform offered by Barcelona(Open Data Barcelona 2022).

As underlined by other studies, conducted by Ayaz and other data scientists, the ILD technology presents other types of limitations. “These sensors have significant limitations in heterogeneous traffic, such as in congested areas, and they are unable to count pedestrians” [6].

RADAR technology, while accurate for measuring vehicle speed, is limited in its ability to provide detailed visual information [9]. It excels in speed measurement but falls short in offering a comprehensive view of traffic patterns. RFID technology, although valuable for object identification and tracking, has limitations in terms of the range at which it operates. Passive RFID tags rely on nearby readers for power and communication, limiting their tracking capabilities in larger areas.

While RADAR and RFID technologies offer efficiency, emerging data analytics mediums are set to define novel standards for describing spatial dynamics. These advanced tools provide deeper insights into spatial phenomena, offering qualitative and quantitative analytical descriptions of usage patterns.

Conclusion

As the Superblock model continues to evolve, the integration of data-driven approaches is becoming increasingly pivotal. The wealth of data collected from urban environments equips policymakers and stakeholders with the necessary tools to make informed decisions that optimize efficiency and enhance the urban experience. In Barcelona and cities worldwide, data-driven urban planning is ushering in a new era of sustainable, effective, and evidence-based interventions. It is through these innovations that we can truly shape the cities of the future.

In the following chapters of this series, we will delve into the rising importance of image analytics and image processing via computer vision and machine learning. These cutting-edge technologies hold the potential to provide richer, more comprehensive data for urban planning. Stay tuned to discover how these innovations are shaping the future of Superblock interventions and urban planning as a whole.

References

1. Monge, F., Barns, S., Kattel, R., & Bria, F. (2022). A new data deal: The case of Barcelona. UCL Institute for Innovation and Public Purpose, (No. WP 2022/02). https://www.ucl.ac.uk/bartlett/public-purpose/publications/2022/feb/new-data-deal-case-barcelona
2. Martínez-Díaz, Margarita. 2022. ‘Accurate, Affordable and Widely Applicable Freeway Travel Time Prediction: Fusing Vehicle Counts with Data Provided by New Monitoring Technologies’. In The Evolution of Travel Time Information Systems: The Role of Comprehensive Traffic Models and Improvements Towards Cooperative Driving Environments, edited by Margarita Martínez-Díaz, 101–38. Springer Tracts on Transportation and Traffic. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-89672-0_4.
3. Monge, F., Barns, S., Kattel, R., & Bria, F. (2022). A new data deal: The case of Barcelona. UCL Institute for Innovation and Public Purpose, (No. WP 2022/02). https://www.ucl.ac.uk/bartlett/public-purpose/publications/2022/feb/new-data-deal-case-barcelona
4. Argota Sánchez-Vaquerizo, Javier. 2022. ‘Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona’s Traffic with Empirical Data’. ISPRS International Journal of Geo-Information 11 (1): 24. https://doi.org/10.3390/ijgi11010024.
5. Singh, Niraj Kumar, Lelitha Vanajakashi, and Arun K. Tangirala. 2018. ‘Segmentation of Vehicle Signatures from Inductive Loop Detector (ILD) Data for Real-Time Traffic Monitoring’. In 2018 10th International Conference on Communication Systems & Networks (COMSNETS), 601–6. https://doi.org/10.1109/COMSNETS.2018.8328281.
6. Ayaz, Shehzad, Hussain Ahmad, Hamza Khan, and Yasir Mehmood. 2021. ‘TRAFFIC FLOW MONITORING FOR HETEROGENEOUS TRAFFIC’ 3 (August): 54–61.
7. Jia, Y., Guo, L., & Wang, X. (2018). 4 — Real-time control systems. In L. Deka & M. Chowdhury (Eds.), Transportation Cyber-Physical Systems (pp. 81–113). Elsevier. https://doi.org/10.1016/B978-0-12-814295-0.00004-6
8. Kumar, S., & Moore, K. B. (2002). The Evolution of Global Positioning System (GPS) Technology. Journal of Science Education and Technology, 11(1), 59–80.
9. Belenguer, Ferran Mocholí, Antonio Martínez Millana, Antonio Mocholí Salcedo, and Victor Milián Sánchez. 2019. ‘Vehicle Modeling for the Analysis of the Response of Detectors Based on Inductive Loops’. PLOS ONE 14 (9): e0218631. https://doi.org/10.1371/journal.pone.0218631.