{"id":21922972,"url":"https://github.com/prof18/tflparser","last_synced_at":"2026-05-01T17:33:42.866Z","repository":{"id":71273167,"uuid":"102014768","full_name":"prof18/TfLParser","owner":"prof18","description":"An bunch of Python Script to parse traffic data available from Traffic For London (TfL)","archived":false,"fork":false,"pushed_at":"2017-08-31T16:35:13.000Z","size":18,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2026-04-30T01:12:09.332Z","etag":null,"topics":["pandas","parser","python"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/prof18.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-08-31T15:18:23.000Z","updated_at":"2017-08-31T16:07:10.000Z","dependencies_parsed_at":null,"dependency_job_id":"810a0966-7d74-457e-a0b7-02dd43a30982","html_url":"https://github.com/prof18/TfLParser","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/prof18/TfLParser","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prof18%2FTfLParser","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prof18%2FTfLParser/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prof18%2FTfLParser/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prof18%2FTfLParser/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/prof18","download_url":"https://codeload.github.com/prof18/TfLParser/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prof18%2FTfLParser/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32507087,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-30T13:12:12.517Z","status":"online","status_checked_at":"2026-05-01T02:00:05.856Z","response_time":64,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["pandas","parser","python"],"created_at":"2024-11-28T21:08:11.953Z","updated_at":"2026-05-01T17:33:42.848Z","avatar_url":"https://github.com/prof18.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Traffic for London (TfL) Parser:\n\nThese parsers analyze data, available from Transport for London (TfL). These data come from the Urban Traffic Control System (UTC), a system used to measure the traffic flow across London to drive the traffic light optimizer system. The data are used to develop a method for the deployment of Road Side Units for a Vehicular Network\n\n# How to use the Parser:\n\n## Parser for the complete detectors folder:\n\n### Scot Parser:\n\nThis parser will extract useful data from scoot_detectors.csv. This file contains information about each separate SCOOT detector. In particular, we will extract: longitude, latitude, topographic identifier, easting (cartesian coordinates), northing (cartesian coordinates) and detector id. With this extracted information we can build a database to retrieve in a faster way the location of the detectors.\n\n### Cleaning Parser:\n\nThis parser analyze all the data from a month and it will create a folder for each day that contains a file for each junction. The structure of this file is the same of the original file but we'll keep only the detectors that have a \"location information\" (i.e. they exist in the file generated by scot_parser.py).\n\n### Single Detector Parser:\n\nThis parser can be used the select only certain detector. It generates a folder for each main detector (e.g. 01-142) that contains the detailed file for each road detector (e.g. 01-142u and 01-142w)\n\n## Parser for the selected detector folder:\n\t\nThese parsers has to be placed inside the directory that contains the following files.\n\nThe file of the single day must be in this type of folder: \n\u003ccounter\u003e. \u003cNumber\u003e Festive Week\n\u003ccounter\u003e. \u003cNumber\u003e Work Week\n\n### Velocity Parser:\n\nTo perform automatically all this points you can run the ./velocity.sh script\n\t\t\t\n1) First of all you need to run the velocity_parser.py to compute the velocity of the single vehicle\n2) Now you can run the velocity_interval_parser.py that computes the mean velocity in a certain time frame\n3) Now you can run the mean_velocity.py parser that computes a mean velocity for the work and festive week, using the velocity in the same time frame used in point 2)\n4) Now you can run the last script, velocity_plot.py that generates a plot starting from the data computed during point 3) \n\t\t\t\n### Interarrival Parser:\n\nTo perform automatically all this points you can run the ./interarrival.sh script\n\t\t\t\n1) First of all you need to run the interarrival_time_interval_parser.py parser that computes the interarrival times in a specific time frame\n2) Now you can run the mean_interarrival.py parser that computes a mean interarrival time for the work and festive week, using the interarrival time in the same time frame used in point 1)\n3) Now you can run the last script, interarrival_plot.py that generates a plot starting from the data computed during point 2)\n\nWith the intearrival_maximum_plot.py you can generate the plot of the global maximum interarrival time\n\n### Vehicle Number Parser:\n\nTo perform automatically all this points you can run the ./vehicle_number.sh script\n\n1) First of all you need to run the vehicle_numer_interval_parser.py that computes the number of vehicle in a specific time frame\n2) Now you can run the mean_vehicle_number.py parser that computes the mean vehicle number for the work and festive week, using the same time frame of point 1)\n3) Now you can run the last script that, vehicle_number_plot.py that generates a plot starting from the data computed during point 2)\n\n### Throughput Parser:\n\nFor every Access Point deployment there is a specific parser, named throughput_parser_conf\u003cnumber\u003e.py. This parser computes the throughput of every access point and generates a plot\n\n### Detector Time Parser:\n\t\t\t\nThis parser computes the time needed to reach a detector from another. The raw data is road_x.csv and inside it there is the placement of each pair of detectors\n\t\t\t\n### Maximum Inter-Attival Plot:\n\nThis parser plots the maximum inter-arrival time between all the detectors\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprof18%2Ftflparser","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprof18%2Ftflparser","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprof18%2Ftflparser/lists"}