Speaker
Description
Sustainable Urban Mobility Planning (SUMP) is a planning approach aimed at improving urban mobility through participatory and evidence-based decision-making. As a result, it requires extensive data collection and transport modeling. However, data collection and modeling for SUMPs face significant challenges particularly in large metropolitan areas of Türkiye. This paper presents challenges encountered during the İzmir and İstanbul SUMP projects, based on the experiences of Parabol Company.
One fundamental obstacle in data collection was data availability and standardization. Mobility data in Turkish cities is often fragmented across multiple public and private entities, including municipal departments, national institutions, private service providers, leading to inconsistencies in formats and methodologies. This lack of coordination created data gaps, hindering the ability to build a holistic understanding of urban mobility patterns. To address this, data harmonization efforts and cross-agency collaboration were initiated. Municipal departments and public-private stakeholders participated in focus group meetings to identify data gaps, and a centralized cloud-based repository was introduced in both cities to integrate diverse datasets related to transport demand, traffic, policy, land use, and socio-demographics. However, bureaucratic constraints and institutional reluctance further complicated data access. Collaboration among departments and institutions was often hindered by concerns over data ownership, necessitating open-data frameworks and standardized data-sharing agreements.
The second challenge was data quality and reliability. Traditional data collection methods, including surveys and manual traffic counts, while essential, faced issues such as high costs, time constraints, human error, and response biases. To overcome these limitations, Parabol developed a GIS-based survey tool called “Mapalyse”, enabling interactive, map-based surveys to activity based travel behaviors. The digitally designed GIS-based survey improved accuracy, location precision, and usability, while an online dashboard ensured real-time quality control, monitoring key indicators and identifying inconsistencies. Additionally, manual traffic counts were supplemented with image processing and other traffic count data from public institutions, reducing human error and enhancing accuracy.
Public engagement was another challange. Surveys were hindered by citizens’ reluctance to share travel data and skepticism regarding its benefits. Awareness campaigns on how mobility data enhances public transport might improve public engagement in data collection for SUMPs in Turkish cities. Additionally, incentivized participation, such as public transport discounts or prize draws, can increase survey response rates together with trust and data quality, while strong data privacy assurances can alleviate concerns about personal data misuse. A centralized framework led by national public institutions can standardize the data collection process for SUMPs, ensuring comparability across cities and regions. For this purpose, the Ministry of Transport and Infrastructure, in collaboration with municipalities and public institutions such as Turkish Statistical Institute, can establish a national mobility data standard that defines common data formats and collection methodologies.
Another key limitation was the lack of granular, high-frequency trip data. To bridge this gap, big data sources were integrated, covering a larger sample size and broader time range than conventional approaches. Bluetooth sensors, license plate recognition systems, smart card data, mobile network data, and GPS data were collected to enhance the accuracy of analysis and transport modeling.
Experiences from the İzmir and İstanbul SUMP projects highlight the importance of a multi-method approach in data collection and modeling. Combining traditional survey techniques with digital tools, fostering cross-institutional collaboration, and leveraging big data were essential for improving the accuracy of mobility analysis and transport modeling. These findings provide lessons for other cities aiming to implement SUMPs, emphasizing the necessity of technological integration, policy support, and inclusive data governance. Moving forward, institutionalizing open-data policies, promoting public-private data-sharing ecosystems, and investing in next-generation mobility intelligence platforms will be critical for ensuring scalable, efficient, and sustainable mobility planning.
| Keywords | SUMP, Data Collection, Transport Modeling, İzmir, İstanbul |
|---|---|
| Best Congress Paper Award | No |
