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Norton","email":"linusnorton@gmail.com"},"license":"GPL-3.0","homepage":"https://github.com/planarnetwork/raptor#readme","keywords":["Journey","planning","algorith","public","transit"],"repository":{"type":"git","url":"git+https://github.com/planarnetwork/raptor.git"},"description":"Implementation of the Round bAsed Public Transit Optimized Router (Raptor) journey planning algorithm.","maintainers":[{"name":"linusnorton","email":"linusnorton@gmail.com"}],"readme":"![Raptor](logo.png)\n\nRaptor Journey Planner\n=========================\n![npm](https://img.shields.io/npm/v/raptor-journey-planner.svg?style=flat-square)\n\nA near direct implementation of the [Round bAsed Public Transit Optimized Router (Raptor)](https://www.microsoft.com/en-us/research/wp-content/uploads/2012/01/raptor_alenex.pdf) journey planning algorithm as described in the paper. \n\nIt contains the range query (rRaptor) algorithm and runs across multiple threads: a range query can\nspread its scans over a pool of workers, and separate depart after queries can be answered\nconcurrently by that same pool. It does not contain the multi-criteria (mcRaptor) variant, although\nresults can be filtered on multiple criteria after they are found.\n\nAdditional features not in the paper implementation:\n - Calendars are checked to ensure services are running on the specified day\n - Multi-day journeys\n - The origin and destination may be a set of stops\n - Interchange time at each station is applied\n - Pickup / set down marker of stop times are obeyed\n - Multi-criteria journey filtering\n - Taking a footpath counts towards the number of changes (journey legs)\n - Journeys are planned between stations, and platforms are retained for display\n\n## Installation\n\nIt will work with any well-formed GTFS data set.\n \nNode 22 or later is required for all examples.\n\n```\nnpm install --save raptor-journey-planner\n```\n\nThe package ships both CommonJS and ES modules, so `require` and `import` both work. The examples\nbelow use `require`; the equivalent `import` is the same names from the same place.\n\n`@gb-transit/gtfs-loader` is the only runtime dependency.\n\n## Queries\n\nEvery query is built the same way: load a feed, create a network from it, then plan.\n\n### Depart After Query\n\nFind the first results that depart after a specific time\n\n```\nconst fs = require(\"fs\");\nconst {loadGTFS, JourneyFactory, createNetwork, DepartAfterQuery} = require(\"raptor-journey-planner\");\n\nconst feed = await loadGTFS(fs.createReadStream(\"gtfs.zip\"));\nconst network = createNetwork(feed);\nconst resultsFactory = new JourneyFactory();\nconst query = new DepartAfterQuery(network, resultsFactory);\nconst journeys = query.plan(\"NRW\", \"STA\", new Date(), 9 * 60 * 60);\n```\n\n### Group Station Depart After Query\n\nFind results from multiple origin and destinations\n\n```\nconst fs = require(\"fs\");\nconst {loadGTFS, JourneyFactory, createNetwork, GroupStationDepartAfterQuery} = require(\"raptor-journey-planner\");\n\nconst feed = await loadGTFS(fs.createReadStream(\"gtfs.zip\"));\nconst network = createNetwork(feed);\nconst resultsFactory = new JourneyFactory();\nconst query = new GroupStationDepartAfterQuery(network, resultsFactory);\nconst journeys = query.plan([\"NRW\"], [\"LST\", \"EUS\"], new Date(), 9 * 60 * 60);\n```\n\n### Range Query\n\nFind results departing between a time range\n\n```\nconst fs = require(\"fs\");\nconst {loadGTFS, JourneyFactory, createNetwork, RangeQuery} = require(\"raptor-journey-planner\");\n\nconst feed = await loadGTFS(fs.createReadStream(\"gtfs.zip\"));\nconst network = createNetwork(feed);\nconst resultsFactory = new JourneyFactory();\nconst query = new RangeQuery(network, resultsFactory);\nconst journeys = query.plan(\"NRW\", \"LST\", new Date(), 9 * 60 * 60, 11 * 60 * 60);\n```\n\n### Parallel Range Query\n\nRuns the scans of a range query on a pool of planners instead of one after another, and stops once\nit has enough journeys.\n\nYou supply the pool. A planner is anything with an async `plan` matching `GroupStationDepartAfterQuery`,\nso it is usually a worker holding its own network, or a `PlannerClient`.\n\n```\nconst {createNetwork, ParallelRangeQuery, MultipleCriteriaFilter} = require(\"raptor-journey-planner\");\n\nconst network = createNetwork(feed);\nconst planners = workers.map(worker => new PlannerClient(worker));\nconst query = new ParallelRangeQuery(network, planners, [new MultipleCriteriaFilter()]);\nconst journeys = await query.plan(\"NRW\", \"LST\", new Date(), 1, 24 * 60 * 60, 20);\n```\n\nThe scans are dispatched in batches the size of the pool, so asking for 20 journeys may return a\nfew more: the batch that reaches the target is finished rather than abandoned.\n\n### Parallel Depart After Query\n\nSends each query to the planner in a pool with the least work outstanding. This answers more\nqueries at once, it does not make a single query faster.\n\n```\nconst {ParallelDepartAfterQuery} = require(\"raptor-journey-planner\");\n\nconst query = new ParallelDepartAfterQuery(planners);\nconst journeys = await query.plan(\"NRW\", \"LST\", new Date(), 9 * 60 * 60);\n```\n\n### Filters\n\nBy default the multi-criteria filter will keep journeys as long as there are no subsequent journeys that arrive sooner and have the same or less changes.\n\n```\nconst fs = require(\"fs\");\nconst {loadGTFS, JourneyFactory, createNetwork, RangeQuery, MultipleCriteriaFilter} = require(\"raptor-journey-planner\");\n\nconst feed = await loadGTFS(fs.createReadStream(\"gtfs.zip\"));\nconst network = createNetwork(feed);\nconst resultsFactory = new JourneyFactory();\nconst filter = new MultipleCriteriaFilter();\nconst maxSearchDays = 3;\nconst query = new RangeQuery(network, resultsFactory, maxSearchDays, [filter]);\nconst journeys = query.plan(\"NRW\", \"LST\", new Date(), 9 * 60 * 60, 11 * 60 * 60);\n```\n\n## Loading a feed\n\n`loadGTFS` takes whatever the environment can give bytes from, and reads the zip as it arrives\nrather than after it has all been collected, so parsing overlaps the download:\n\n```js\nawait loadGTFS(fs.createReadStream(\"gtfs.zip\"));  // a node stream\nawait loadGTFS(await file.arrayBuffer());          // bytes\nawait loadGTFS(fileInput.files[0]);                // a File or Blob\nawait loadGTFS(await fetch(url));                  // a Response\n```\n\nor `loadGTFSFromUrl`, which does the fetch for you:\n\n```js\nconst feed = await loadGTFSFromUrl(\"https://example.com/gtfs.zip\", {\n  onProgress: p => console.log(`${p.phase} ${p.entry ?? \"\"} ${p.rows} rows`)\n});\n```\n\n`onProgress` reports the bytes read, the file being read and how far through it we are, and the\nnumber of rows so far. It is throttled to `progressInterval` milliseconds, 100 by default, so it\nis safe to render from directly.\n\n## In the browser\n\nLoading a feed is slow enough to be worth keeping off the main thread. `PlannerClient` talks to a\nworker that holds the feed and the timetable, so only the journeys you ask for cross back:\n\n```js\nimport { PlannerClient } from \"raptor-journey-planner\";\n\nconst worker = new Worker(new URL(\"raptor-journey-planner/worker\", import.meta.url), { type: \"module\" });\nconst planner = new PlannerClient(worker);\n\nawait planner.load({ url: \"/gtfs.zip\" }, { onProgress: p => setProgress(p) });\n\nconst journeys = await planner.plan([\"NRW\"], [\"LST\"], new Date(), 9 * 60 * 60);\n```\n\nYou construct the `Worker` yourself because resolving a worker's URL is a question for whatever is\nbundling your application, and any helper here would only ever be right for one bundler. The entry\npoint is a plain ES module, which every bundler understands and a browser can load natively.\n\nTwo things to know about what comes back. The journeys are copies, so a leg's `trip` is not the\nsame object the worker holds and cannot be used as a key against one. And a trip arrives without\nits `service`: a class does not survive being posted, so it would arrive with its fields and\nwithout its `runsOn`, which is worse than leaving it out. Whether a trip runs on a date is settled\ninside the worker before the journey is returned.\n\n`load` hands back the feed's routes and agencies along with what it loaded. A leg names the route\nits trip runs on and stops there, so these are what turn that id into a route and an operator. They\nare sent once, when the feed loads, because a feed has few of them — the GB feed has 98 routes and\n41 agencies against 290,640 trips — and copying an operator's name onto every leg of every journey\nwould cost more:\n\n```js\nconst feed = await planner.load({ url: \"/gtfs.zip\" });\nconst [journey] = await planner.plan([\"PAD\"], [\"PLY\"], new Date(), 9 * 60 * 60);\nconst { trip } = journey.legs[0];\nconst route = feed.routes[trip.routeId];\n\n`${trip.shortName} to ${trip.headsign}, ${feed.agencies[route.agencyId].name}`;\n// \"GW130700 to Plymouth, GWR\"\n```\n\nA feed need not name either, so `routeId` may be missing and the indexes empty.\n\n`load` hands back the feed's areas too — `areas.txt` and `stop_areas.txt` read as one thing. Nothing\nthe algorithm does refers to one: an area is a fares construct, and no journey mentions it. It is\nhere because a group station has nowhere else to live. GTFS has no station of stations —\n`parent_station` is forbidden on a station and the hierarchy is one level deep — so \"London\nTerminals\" is an area holding eighteen stops rather than a parent of them, and `transfers.txt` is\nthe wrong tool for the job because it asserts a rider can walk between the two stops, which Euston\nand Waterloo are not.\n\nAn area names stop ids as the feed wrote them, and a query is asked in stations, so `stops()`\nresolves one to the other. Both come from the same load, so planning between group stations needs\nno second reading of the zip:\n\n```js\nconst feed = await planner.load({ url: \"/gtfs.zip\" });\nconst stops = await planner.stops();\nconst byId = new Map(stops.map(stop => [stop.id, stop]));\n\nconst terminals = feed.areas[\"1072\"].stops.map(id => byId.get(id).code);\nawait planner.plan([\"BTN\"], terminals, new Date(), 9 * 60 * 60);\n```\n\nThe index is empty for a feed with no `areas.txt`, which most feeds are.\n\nPassing `{ date }` to `load` restricts the timetable to that date, which makes it smaller and\nqueries faster.\n\n## Stops and stations\n\nJourneys are planned between stations, because that is where interchange time and transfers are\ndefined and the only place a change of vehicle is possible. `loadGTFS` returns the feed exactly as\nit finds it; stops are resolved to their station when the timetable is built, following\n`parent_station` up through however many levels of grouping the feed uses.\n\nStations are identified by their `stop_code`, falling back to `stop_id` where a feed does not give\none. That is the identifier queries are made with and journeys are returned in. A feed that\nidentifies platforms has to give its stations ids of its own, and `stop_code` is where it puts the\ncode the station is actually known by — a CRS code for UK rail. GTFS places no uniqueness\nrequirement on `stop_code`, so a feed that gives two stations the same one is rejected when the\ntimetable is created, rather than silently planning them as the same place.\n\nNothing is lost, because nothing is rewritten. The trips a journey hands back are the feed's own,\nso a leg's stop times carry the feed's `stop_id` — the platform, where the feed identifies one —\nand include the passing points between the stop boarded at and the stop alighted at. Filter on\n`pickUp` or `dropOff` for the calls a passenger can use, and `network.stations` maps a stop to the\nstation the algorithm planned it as.\n\n## Dates\n\nThe timetable holds a calendar for a fixed period, taken from `feed_info.txt` where the feed gives\none and 120 days from today where it does not. Planning for a date outside that period throws\nrather than quietly finding nothing. To plan for a different period, set `feedInfo` on the feed\nbefore building:\n\n```\nconst feed = await loadGTFS(fs.createReadStream(\"gtfs.zip\"));\nconst network = createNetwork({ ...feed, feedInfo: { startDate: 20250901, endDate: 20251101 } });\n```\n\n## Contributing\n\nIssues and PRs are very welcome. To get the project set up run:\n\n```\ngit clone git@github.com:planarnetwork/raptor\nnpm install --dev\nnpm test\n```\n\nIf you would like to send a pull request please write your contribution in TypeScript and if possible, add a test.\n\n## License\n\nThis software is licensed under [GNU GPLv3](https://www.gnu.org/licenses/gpl-3.0.en.html).\n","readmeFilename":"README.md"}