Data-Driven Approach for Urban Micromobility Enhancement through Safety Mapping and Intelligent Route Planning
Abstract
:1. Introduction
2. Background
2.1. Micromobility Expansion in Cities
2.2. Micromobility Safety Issues
2.3. NVIDIA Semantic Segmentation
2.4. YOLOv5
2.5. Bikeable
2.6. OpenRouteService and OpenStreetMap
3. Materials and Methods
- Preparation: The study zone was selected in this stage, and boundaries are defined based on the geographic data of the region under analysis. Random latitude and longitude coordinates were generated using a uniform distribution.
- Data Collection: Using the GSV API, the data (images) necessary for processing were collected. The API retrieved four images for each generated location, capturing the full surroundings at 90, 180, 270, and 360 degrees. The API was specified to retrieve outdoor images only. Metadata, such as coordinates and the date of the image, was extracted, and metadata and images were saved for further processing.
- Processing: This stage involved applying image semantic segmentation (NVIDIA Image Semantic Segmentation) and object detection (YOLOv5x6) techniques to identify and classify structures and objects within the images. This information was crucial for determining potential safety risks in the urban environment. Points where no objects were detected in any of the images from the four different angles were discarded. The list of segmented classes is in Appendix A. Additionally, a list of detected objects can be found in Appendix B.
- Outputs: A safety map was created at this stage, which served as the basis for the safe route planner and was designed to help users easily understand the safety levels of different areas in the city. Additionally, a safe route generator was developed and made available for users. The generator avoids locations based on the user’s selected safety options when routing.
3.1. Safety Score
3.2. Routing
4. Case Study
4.1. Cycling Infrastructure Improvement
4.2. Safety Score Prediction
4.3. Router Factor
- A total of 100 pairs of random points are generated, each 5 km apart when measured in a straight line. The methodology for generating these pairs is described in detail in the subsequent sub-chapter;
- Routes are calculated for origin and destination pairs based on time and distance;
- Following this, the routing factor for each route is computed.
- s.t.
- TravelTime ≈ 15 min
- TotalDistance ≥ 5 km
- where k is the total routes (100 random routes), TravelTime is the duration of the route calculated by the router, and TotalDistance is the distance calculated by the router.
4.4. Random Route Generation
- Determine the study area: The initial step involves defining the boundaries of the study area, which is Lisbon, Portugal in this case.
- Create a grid of points: Using these boundaries, a grid is constructed consisting of points that are spaced every 500 m. The grid is confined within the previously established boundaries, resulting in a total of 442 points distributed throughout the study area.
- Calculate distances between all points: Subsequent to the grid’s creation, the distances between each point and every other point on the grid are calculated. Applying the mathematical concept of combinations, all unique pairings of points are determined, leading to a total of 97,581 routes.
- Filtering routes: Given the aim of generating routes with a specific distance, pairs of points where the Euclidean distance between them is approximately 5 km, with a tolerance of ±10 m, are filtered out. This step narrows down the number of suitable routes to 1168. Routes with routing errors have been excluded. Additionally, routes for which the safest path could not be computed due to an excessive number of risk points were excluded from the analysis.
- Randomly choose routes: Finally, 100 routes are randomly selected from the list of suitable ones. This random selection ensures a diverse set of routes distributed throughout the study area.
5. Results and Discussion
5.1. Safety Score Prediction
5.2. Route Planning and Comparative Analysis
5.3. Limitations
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. List of Segmented Classes
1. Bicycle | 24. Curb | 47. Rail Track |
2. Bicyclist | 25. Curb Cut | 48. Road |
3. Bike Lane | 26. Crosswalk—Plain | 49. Sand |
4. Bike Rack | 27. Ego Vehicle | 50. Service Lane |
5. Billboard | 28. Fence | 51. Sidewalk |
6. Bird | 29. Fire Hydrant | 52. Sky |
7. Boat | 30. Guard Rail | 53. Snow |
8. Boat Mount | 31. Junction Box | 54. Street Light |
9. Brid | 32. Lane Marking—Crosswalk | 55. Terrain |
10. Bridge | 33. Lane Marking—General | 56. Traffic Light |
11. Building | 34. Mailbox | 57. Traffic Sign (Back) |
12. Bus | 35. Manhole | 58. Traffic Sign (Front) |
13. Banner | 36. Mountain | 59. Traffic Sign Frame |
14. Barrier | 37. Motorcycle | 60. Trailer |
15. Bench | 38. Motorcyclist | 61. Trash Can |
16. Bicycle | 39. On Rails | 62. Truck |
17. Boat | 40. Other Rider | 63. Tunnel |
18. Bus | 41. Other Vehicle | 64. Unlabeled |
19. Car | 42. Parking | 65. Utility Pole |
20. Car Mount | 43. Pedestrian Area | 66. Vegetation |
21. Caravan | 44. Phone Booth | 67. Water |
22. Catch Basin | 45. Pole | 68. Wheeled Slow |
23. CCTV Camera | 46. Pothole |
Appendix B. List of Detected Objects
1. Airplane | 24. Donut | 47. Sink |
2. Apple | 25. Elephant | 48. Skateboard |
3. Backpack | 26. Fire Hydrant | 49. Skis |
4. Banana | 27. Frisbee | 50. Snowboard |
5. Baseball Bat | 28. Hair Drier | 51. Spoon |
6. Baseball Glove | 29. Handbag | 52. Sports Ball |
7. Bear | 30. Horse | 53. Stop Sign |
8. Bed | 31. Hot Dog | 54. Suitcase |
9. Bird | 32. Keyboard | 55. Surfboard |
10. Boat | 33. Kite | 56. Teddy Bear |
11. Book | 34. Knife | 57. Television |
12. Bottle | 35. Laptop | 58. Tennis Racket |
13. Bowl | 36. Microwave | 59. Toaster |
14. Broccoli | 37. Motorcycle | 60. Toilet |
15. Cell Phone | 38. Mouse | 61. Toothbrush |
16. Chair | 39. Oven | 62. Tie |
17. Cat | 40. Parking Meter | 63. Toilet |
18. Clock | 41. Person | 64. Traffic Light |
19. Couch | 42. Potted Plant | 65. Train |
20. Cow | 43. Remote | 66. Truck |
21. Cup | 44. Sandwich | 67. Umbrella |
22. Dining Table | 45. Scissors | 68. Vase |
23. Dog | 46. Sheep | 69. Wine Glass |
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Parish | Area (km2) | Number of Points | Points/km2 | Safety Score |
---|---|---|---|---|
Ajuda | 2.88 | 114 | 40 | 4.3 |
Alcântara | 5.07 | 170 | 34 | 4.2 |
Alvalade | 5.34 | 397 | 74 | 4.7 |
Areeiro | 1.72 | 155 | 90 | 4.7 |
Arroios | 2.13 | 277 | 130 | 4.5 |
Avenidas Novas | 2.99 | 296 | 99 | 4.6 |
Beato | 2.48 | 84 | 34 | 4.5 |
Belém | 10.43 | 350 | 34 | 4.5 |
Benfica | 8.02 | 294 | 37 | 4.4 |
Campo de Ourique | 1.65 | 125 | 76 | 4.3 |
Campolide | 2.77 | 143 | 52 | 4.3 |
Carnide | 3.69 | 168 | 46 | 4.8 |
Estrela | 4.60 | 194 | 42 | 4.5 |
Lumiar | 6.57 | 385 | 59 | 4.6 |
Marvila | 7.12 | 359 | 50 | 4.6 |
Misericórdia | 2.19 | 66 | 30 | 4.4 |
Olivais | 8.09 | 405 | 50 | 4.7 |
Parque das Nações | 5.43 | 240 | 44 | 4.6 |
Penha de França | 2.71 | 168 | 62 | 4.5 |
Santa Clara | 3.36 | 143 | 43 | 4.7 |
Santa Maria Maior | 3.01 | 114 | 38 | 4.3 |
Santo António | 1.49 | 134 | 90 | 4.7 |
São Domingos de Benfica | 4.29 | 255 | 59 | 4.6 |
São Vicente | 1.99 | 96 | 48 | 4.5 |
Average | 216 | 60 | 4.5 |
Variable | Benchmark | Shortest | Balanced | Safest |
---|---|---|---|---|
Time (min) | 25.25 ± 4.30 | 21.77 ± 2.58 | 21.94 ± 2.62 | 23.47 ± 3.10 |
Distance (km) | 6.62 ± 1.04 | 6.29 ± 0.71 | 6.33 ± 0.71 | 6.70 ± 0.81 |
Route factor | 1.54 ± 0.25 | 1.37 ± 0.16 | 1.38 ± 0.16 | 1.47 ± 0.19 |
% change in time | - | −13.7% | −13.1% | −7.1% |
% change in distance | - | −5.0% | −4.4% | 1.2% |
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Tamagusko, T.; Gomes Correia, M.; Rita, L.; Bostan, T.-C.; Peliteiro, M.; Martins, R.; Santos, L.; Ferreira, A. Data-Driven Approach for Urban Micromobility Enhancement through Safety Mapping and Intelligent Route Planning. Smart Cities 2023, 6, 2035-2056. https://doi.org/10.3390/smartcities6040094
Tamagusko T, Gomes Correia M, Rita L, Bostan T-C, Peliteiro M, Martins R, Santos L, Ferreira A. Data-Driven Approach for Urban Micromobility Enhancement through Safety Mapping and Intelligent Route Planning. Smart Cities. 2023; 6(4):2035-2056. https://doi.org/10.3390/smartcities6040094
Chicago/Turabian StyleTamagusko, Tiago, Matheus Gomes Correia, Luís Rita, Tudor-Codrin Bostan, Miguel Peliteiro, Rodrigo Martins, Luísa Santos, and Adelino Ferreira. 2023. "Data-Driven Approach for Urban Micromobility Enhancement through Safety Mapping and Intelligent Route Planning" Smart Cities 6, no. 4: 2035-2056. https://doi.org/10.3390/smartcities6040094