journal article·2025-12-17Open access

Discovery of Upcoming Cross Streets in Google Maps Blind Navigation

Apostolos Meliones, Georgios Mantzoros

Applied Sciences· Volume 15· Issue 24

Abstract

This paper describes an application aimed at discovering cross streets within navigation routes by using the capabilities of the Google Maps API. The application’s core functionality revolves around locating the name of the succeeding cross street in the direction of the user, determined by their current location. To extract this information, the application synergizes the Nearest Roads library with the Geocoding API, utilizing a pair of points situated at an angle within a circular boundary centered on the user’s position, derived through the haversine formula. To ascertain optimal parameters for the circle’s radius and angle, a thorough sampling process encompassing 100 real-world instances from four Greek cities, Athens, Thessaloniki, Patras, and Karystos, was conducted. These cities were selected for their varying urban characteristics, enabling a comprehensive evaluation of the application’s performance across diverse street network complexities. The findings highlight the most effective degrees and radius parameters which exhibit an efficient success rate exceeding 91% in accurately finding the next cross street within navigation routes across the sampled cities. This paper provides a comprehensive account of the solution methodology, the process of sampling locations across diverse urban settings, and the resultant findings.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Automated Road and Building Extraction · Data Management and Algorithms · Geographic Information Systems Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece

References (39)

Show source-provided references
1. Theodorou, P., Tsiligkos, K., Meliones, A., and Filios, C. (2022). An Extended Usability and UX Evaluation of a Mobile Application for the Navigation of Individuals with Blindness and Visual Impairments Outdoors—An Evaluation Framework Based on Training. Sensors, 22.10.3390/s22124538 ↗
2. (2025, December 11). The MANTO Project Webpage. Available online: https://manto.ds.unipi.gr.
3. Smart traffic lights for people with visual impairments: A literature overview and a proposed implementation10.1177/02646196221099154 ↗
4. Meliones, A., Filios, C., and Llorente, J. (2022). Reliable Ultrasonic Obstacle Recognition for Outdoor Blind Navigation. Technologies, 10.10.3390/technologies10030054 ↗
5. (2025, December 11). Lighthouse for the Blind of Greece. Available online: https://fte.gr/en/.
6. (2025, December 11). Google Maps Platform. Available online: https://developers.google.com/maps.
7. (2025, December 11). The BlindSquare Webpage. Available online: https://www.blindsquare.com.
8. (2025, December 11). OpenStreetMap. Available online: https://www.openstreetmap.org/.
9. The Potential of Maps APIs for Internet GIS Applications10.1111/j.1467-9671.2008.01094.x ↗
10. A location-based services and Google maps-based information master system for tour guiding10.1016/j.compeleceng.2015.11.020 ↗
11. Harja, Y.D., and Sarno, R. (2018, January 6–7). Determine the best option for nearest medical services using Google maps API, Haversine and TOPSIS algorithm. Proceedings of the 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia.
12. The Cosine-Haversine Formula10.2307/2309088 ↗
13. Meliones, A., and Maidonis, S. (July, January 30). DALÍ: A Digital Assistant for the Elderly and Visually Impaired using Alexa Speech Interaction and TV Display. Proceedings of the 2020 ACM International Conference on Pervasive Technologies Related to Assistive Environments, Corfu, Greece.10.1145/3389189.3397972 ↗
14. (2025, December 11). Google Maps API, Geocoding API. Available online: https://developers.google.com/maps/documentation/geocoding/overview.
15. (2025, December 11). Google Maps API, Nearest Roads Library. Available online: https://developers.google.com/maps/documentation/roads/nearest.
16. Detecting Road Intersections from GPS Traces10.1007/978-3-642-15300-6_5 ↗
17. Zhang, Z., Huang, X., Sun, C., Zheng, S., Hu, B., Varadarajan, J., Yin, Y., Zimmermann, R., and Wang, G. (2019, January 10–13). Sextant: Grab’s Scalable In-Memory Spatial Data Store for Real-Time K-Nearest Neighbour Search. Proceedings of the 2019 International Conference on Mobile Data Management, Hong Kong, China.10.1109/MDM.2019.00-51 ↗
18. Xie, X., and Philips, W. (2017). Road Intersection Detection through Finding Common Sub-Tracks between Pairwise GNSS Traces. ISPRS Int. J. Geo-Inf., 6.10.3390/ijgi6100311 ↗
19. Yin, Y., Sunderrajan, A., Huang, X., Varadarajan, J., Wang, G., Sahrawat, D., Zhang, Y., Zimmermann, R., and Ng, S.K. (2019, January 5). Multi-scale Graph Convolutional Network for Intersection Detection from GPS Trajectories. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI 20’19), Chicago, IL, USA.10.1145/3356471.3365234 ↗
20. Using Satellite Images Datasets for Road Intersection Detection in Route Planning
21. Eltaher, F., Miralles-Pechuán, L., Courtney, J., and Mckeever, S. (2023, January 27–31). Detecting Road Intersections from Satellite Images using Convolutional Neural Networks. Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC’23), Tallinn, Estonia.10.1145/3555776.3578728 ↗
22. Generation of Istanbul road data set using Google Map API for deep learning-based segmentation10.1080/01431161.2022.2068989 ↗
23. Senousi, A.M., Ahmed, W., Liu, X., and Darwish, W. (2025). Automated Digitization Approach for Road Intersections Mapping: Leveraging Azimuth and Curve Detection from Geo-Spatial Data. ISPRS Int. J. Geo-Inf., 14.10.3390/ijgi14070264 ↗
24. Tran, N.H.K., Berrio, J.S., Shan, M., Ming, Z., and Worrall, S. (2025). LiDAR-based Intersection Localization using Road Structure. arXiv, Available online: https://arxiv.org/html/2505.00512v1.
25. Tran, N.H.K., Berrio, J.S., Shan, M., and Worrall, S. (2025). InterKey: Cross-modal Intersection Keypoints for Global Localization on OpenStreetMap. arXiv, Available online: https://arxiv.org/html/2509.13857v1.
26. Bhatt, D., Sodhi, D., Pal, A., Balasubramanian, V., and Krishna, M. (2017, January 24–28). Have i reached the intersection: A deep learning-based approach for intersection detection from monocular cameras. Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.10.1109/IROS.2017.8206317 ↗
27. Chen, X., Xiang, L., Jiao, F., and Wu, H. (2023). Detecting Turning Relationships and Time Restrictions of OSM Road Intersections from Crowdsourced Trajectories. ISPRS Int. J. Geo-Inf., 12.10.3390/ijgi12090372 ↗
28. Road Network Construction with Complex Intersections Based on Sparsely Sampled Private Car Trajectory Data10.1145/3326060 ↗
29. Fusco, G., Shen, H., and Coughlan, J.M. (2014, January 6–9). Self-Localization at Street Intersections. Proceedings of the Conference on Computer and Robot Vision, Montreal, QC, Canada.10.1109/CRV.2014.14 ↗
30. Eltaher, F., Miralles-Pechuán, L., Courtney, J., and Mckeever, S. (2024, January 26–28). SafeRoute: A Safer Outdoor Navigation Algorithm with Smart Routing for People with Visual Impairment. Proceedings of the 17th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA ’24), Crete, Greece.10.1145/3652037.3652043 ↗
31. Wiener, W., Tikkun, S.R., and Thurman, J. (2025, December 12). IOT Solutions for Near Horizon Challenges in Smart City Pedestrian Travel (Task 2.3). North Carolina Central University, Project FHWA/NC/2020-60. August 2023, Available online: https://connect.ncdot.gov/projects/research/RNAProjDocs/IOT%20Solutions%20for%20Near%20Horizon%20Challenges%20in%20Smart%20City%20Pedestrian%20Travel%20final.pdf.
32. Jain, G., Hindi, B., Xie, M., Zhang, Z., Srinivasula, K., Ghasemi, M., Weiner, D., Xu, X., Paris, S., and Tedjo, C. (2023, January 22–25). Towards Street Camera-based Outdoor Navigation for Blind Pedestrians. Proceedings of the 25th International ACM SIGACCESS Conference on Computers & Accessibility, New York, NY, USA.10.1145/3597638.3614498 ↗
33. Bhargava, B., Angin, P., and Duan, L. (2025, December 12). A Mobile-Cloud Pedestrian Crossing Guide for the Blind, International Conference on Advances in Computing & Communication (ICACC-11), NIT Hamirpur. April 2011. Available online: https://www.cs.purdue.edu/homes/bb/pedestrian_crossing.pdf.
34. Rousell, A., and Zipf, A. (2017). Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data. ISPRS Int. J. Geo-Inf., 6.10.3390/ijgi6030064 ↗
35. Froehlich, J.E., Fiannaca, A.J., Jaber, N.M., Tsaran, V., and Kane, S.K. (October, January 28). StreetViewAI: Making Street View Accessible Using Context-Aware Multimodal AI. Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST ‘25), Busan, Republic of Korea.
36. (2025, November 20). Google Maps Platform Pricing. Available online: https://mapsplatform.google.com/pricing/.
37. (2025, November 20). Google Maps Platform Pay-as-you-go. Available online: https://developers.google.com/maps/billing-and-pricing/pay-as-you-go.
38. (2025, December 11). Open Source Routing Machine (OSRM). Available online: https://project-osrm.org/.
39. Resolving the Shortest Path Problem Using the Haversine Algorithm