A journey planner , trip planner , or route planner is a specialized search engine used to find an optimal means of traveling between two or more given locations, sometimes using more than one mode transport .   Searches May be we Optimized different criteria, for example fastest , shortest , least exchange , cheapest .  They may be constrained for example to leave or arrive at a certain time, to avoid certain waypoints, etc. A single journey may use a sequence of several modes of transport , meaning que le system May know about public transportservices as well as transportation networks for private transportation. Journey planning is sometimes distinguished from route planning ,  where route planning is typically thought of as using , driving , walking or cycling . Journey schedule by contrast Would Makes use of at least one public transportation fashion Operates qui selon published schedules . Journey planning is sometimes distinguished from route planning ,  where route planning is typically thought of as using , driving , walking or cycling . Journey schedule by contrast Would Makes use of at least one public transportation fashion Operates qui selon published schedules . Journey planning is sometimes distinguished from route planning ,  where route planning is typically thought of as using , driving , walking or cycling . Journey schedule by contrast Would Makes use of at least one public transportation fashion Operates qui selon published schedules .
Journey planners have been widely used in the travel industry since the 1970s by booking agents [ citation needed ] . The growth of the Internet , the proliferation of geospatial data , and the development of information technologies has led to the rapid development of many self-service browser-based on-line intermodal trip planners such as Rome2rio , Google Transit , FromAtoB.com and DistancesBetween.com .
A journey planner may be used in conjunction with ticketing and reservation systems.
There is a great deal of variation among different journey planner applications, yet many share some common features. These typically include an interface for helping the user to identify their origin and destination locations. These include a May geocoder qui can find rentals from street addresses or a web map That users can click on. A rent finding process will typically first resolve the origin and destination into the nearest known nodes on the transport network in order to compute a journey plan over its data set of known journeys.
There are a number of options to choose from:
- Desired transport mode
- Time and / or date of travel
- Other criteria
- For flights: cheapest ticket vs. Shortest trip
- For bicycles: shortest path vs. Avoiding traffic
- For transit: shortest trip vs. Minimizing transfers
After the journey planner has computed a trip or set of trips for the user to choose from, these are usually displayed either on a map (for most modes) or in a list (typical for flights). Often a turns, or change modes.
Some daytime planners offer features like highly interactive maps, timetable displays for public transit modes, real-time traffic conditions, and suggested alternate routes. A journey planner may also have more than one user interface , with each optimized for different purposes. For example, online self-service done with a web browser , an interface for call center agents, one for use on mobile devices, or special interfaces for visually impaired users.
Some commercial journey planners include aspects of discovery shopping for accommodation and activities and price comparison for some aspects of a trip.
The first digital public transport was developed by Eduard Tulp, a computer science student at the University of Amsterdam on an Atari PC.  He was hired by the Dutch Railways to build a digital journey planner for train services. In 1990 the first digital journey planner for the Dutch Railways (on diskette) was sold to PC and computers for off-line consultation.  The principle of the software program was published in a Dutch university paper in 1991. This was soon expanded to include all public transport in the Netherlands. Other European countries soon followed with their own day planners. The software is a telecommunications company. Before, experienced staff was needed with good geographical knowledge and experience in consulting the paper timetables.
Early journey planning engines were typically developed as part of the booking systems for high value and transport, using mainframe databases and OLTP systems. Well-known examples of such computer reservations system(CRS) include Saber , Amadeus , Galileo , and the Rail Journey Information System developed by British Rail . As computing resources became widely available, journey planner engines were developed to run on minicomputers , personal computers , and mobile devices , and as internet based services accessible though web browsers ,
In the early 2000s large scale metropolitan web planners such as Transport for London’s journey planner has become available. Starting in 2000 the Traveline service provided all parts of the UK with multi-modal journey planning and in 2003 the Transport Direct portal was one of the first nationwide systems.
Many entities, including municipal government, and for-profit corporations now operate web sites offering trip planning services for large metropolitan areas, or even whole countries [ citation needed ] . Transportation companies such as EasyJet , National Rail Inquiries or Deutsche Bahn .
As the size of the transport systems covered by daytime planners has increased, protocols and algorithms for distributed day-to-day planning have been developed, allowing the computing of dayneys computing devices of the day for different parts of the country. JourneyWeb , EU Spirit , Xephos , and the Delfi are all examples of distributed day-to-day planning protocols. Another development in the 2000s has been the addition of real-time information to update the current schedules to include any delays or changes that will affect the day plan.
Public transport routing
For public transport routing the journey planner is constrained by times of arrival or departure. It may also support different routing criteria – for example, fastest route, least changes, cheapest, etc. Most trip planners are not capable of multiple simultaneous optimizations (eg cheapest , or most flexible ) but may be able to advise fares for a single trip.
The planning of road legs is sometimes done by a separate subsystem within a journey planner, but may consider both modes of calculations as well as intermodal scenarios (eg Park and Ride , kiss and ride , etc.). Typical optimizations for road routing are shortest route , fastest route , cheapest route and with constraints for specific waypoints. Some advanced day-to-day planners can take into account average daytime times on road sections, or even real-time predicted average journey times on road sections.
A journey planner will ideally provide detailed routing for pedestrian access to stops, stations, points of interest etc. This will include, for example, ‘No steps’, ‘wheelchair access’, ‘no lifts’, etc.
Some journey planning systems can calculate bicycle roads,  integrating all paths accessible by bicycle and with additional information like topography, traffic, on-street cycling infrastructure, etc. These systems assume, or allow the user to specify, preferences for quiet or safe roads, minimal elevation change, bicycle lanes , etc.
Journey planners are no better than the data they have available to them. Some journey planners integrate many different kinds of data from numerous sources. The only way to get around is to get to the city center.
Street network data
The most basic journey planners, sometimes referred to as road planners , work only with street network data. Such data can come from one or more public, gold trading crowdsourced datasets Such As TIGER , Esri Gold OpenStreetMap .
Public transport timetables
Data on public transport comes in a number of different standard formats Such As GTFS gold Transmodel . Transit networks can be connected to a street network by creating links between stop or station locations and nearby streets. 
Real-time prediction information
Journey planners may be able to incorporate real-time information into their database and consider them in the selection of optimal routes.  Automatic vehicle location (AVL) systems monitor the position of vehicles using GPS systems and can pass on real-time and information to the journey planning system.  A journey planner May use a real time interface Such as Service Interface for Real Time Information to obtenir this data. Real time road UTMC. Based on this information the journey planner is able to indicate the punctuality or delays for each mode of transport in a departure monitor.
A situation is a software representation of an incident [ citation needed ] (for example security alert, cancellation or bad weather) or event that is affecting or is likely to affect the transport network. A journey planner can integrate situation and use it both to revise its journey planning computations and to annotate its responses so as to inform users through both text and map representations. A journey planner will typically use a standard interface such as SIRI , TPEG or DATEX II to obtain situation information.
Incidents are captured through an incident capturing system (ICS) by different operators and stakeholders, for example in transport operator control rooms, by broadcasters or by the emergency services. Text and image information can be combined with the trip result. Recent incidents can be seen within the routing as well as visualized in an interactive map.
Typically, the use of an inexpensive and efficient method for the detection of a large number of paths. Database queries may also be used where the number of nodes needed to compute a day is small, and to access ancillary information relating to the journey. A single engine peut Entire the transportation network, and Its schedules, or May allow the distributed computation of journeys using a distributed protocol journey schedule Such As JourneyWebgold Delfi Protocol . A journey planning engine may be accessed by different front ends, using a software protocol or application program interface for dayney queries,
The development of journey schedule engines has gone hand in hand with the development of data standards for Representing the stops, routes and timetables of the network, Such As TransXChange , NaPTAN , Transmodelgold GTFS That Ensure That thesis made together. Journey planning algorithms are computational complexity theory . Real-world implementations involve a tradeoff of computational resources between accuracy, completeness of the answer and the time required for calculation. 
The sub-problem of road planning is an easier problem to solve  as it involves less data and fewer constraints. However, with the development of “road timetables”, associating different times for road links at different times of day.
Journey planners use a routing algorithm to search a graph representing the transport network. In the simplest case, the graphs (directed) edges to represent street / path segments and nodes to represent intersections . Routing is Such a graph Can Be Accomplished Effectively using’any of a number of routing algorithms Such As Dijkstra’s , A * , Floyd-Warshall , Golden Johnson’s algorithm .  Different weightings such as distance, cost or accessibility may be associated with each edge, and sometimes with nodes.
When time-dependent features are available, they can be used as RAPTOR 
Distribution companies May Incorporate road schedule software into Their fleet management systems to optimize efficiency road. A road planning setup for distribution companies will often include GPS tracking capability and advanced reporting features that enable dispatchers to prevent unplanned stops, reduce mileage, and plan more fuel-efficient routes.
- Google Transit
- Transport Direct
- Automotive navigation system
- Travel technology
- Public transport route planner
- Service Interface for Real Time Information
- Intelligent Transport Systems
- Multimodal transport
- ^ Jump up to:a b c Li, Jing-Quan; Zhou, Kun; Zhang, Liping; Zhang, Wei-Bin (2012-04-01). “A Multimodal Trip Planning System with Real-Time Traffic and Transit Information” . Journal of Intelligent Transportation Systems . 16 (2): 60-69. ISSN 1547-2450 . Doi : 10.1080 / 15472450.2012.671708 .
- ^ Jump up to:a b Zografos, Konstantinos; Spitadakis, Vassilis; Androutsopoulos, Konstantinos (2008-12-01). “Integrated Passenger Information System for Multimodal Trip Planning” . Transportation Research Record: Journal of the Transportation Research Board . 2072 : 20-29. ISSN 0361-1981 . Doi : 10.3141 / 2072-03 .
- Jump up^ “Bike Triangle | OpenTripPlanner” . GitHub . Retrieved 2017-05-11 .
- ^ Jump up to:a b Bast, Hannah; Delling, Daniel; Goldberg, Andrew; Müller-Hannemann, Matthias; Pajor, Thomas; Sanders, Peter; Wagner, Dorothea; Werneck, Renato F. (2016-01-01). Kliemann, Lasse; Sanders, Peter, eds. Algorithm Engineering . Lecture Notes in Computer Science. Springer International Publishing. pp. 19-80. ISBN 9783319494869 . Doi : 10.1007 / 978-3-319-49487-6_2 .
- Jump up^ Trouw, 05/06/1998
- Jump up^ 175 years of travel information, chapter: Wel of geen vervoer?
- Jump up^ http://kinkrsoftware.nl/contrib/Artikel16b.2a/tulp.pdf, Tulp, Eduard,Searching time-table networks, proefschriftVrije Universiteit Amsterdam, 1991
- Jump up^ Yoon, Ji Won; Pinelli, Fabio; Calabrese, Francesco (2012). “Cityride: A Predictive Bike Sharing Journey Advisor” . Mobile Data Management (MDM), 2012 IEEE 13th International Conference : 306-311.
- Jump up^ Delling, Daniel; Sanders, Peter; Schultes, Dominik; Wagner, Dorothea (2009-01-01). “Engineering Route Planning Algorithms”. In Lerner, Jürgen; Wagner, Dorothea; Zweig, Katharina A. Algorithms of Large and Complex Networks . Lecture Notes in Computer Science. Springer Berlin Heidelberg. pp. 117-139. ISBN 9783642020933 . Doi : 10.1007 / 978-3-642-02094-0_7 .
- Jump up^ “Routing Functions – pgRouting Manual (2.0.0)” . Docs.pgrouting.org . Retrieved 2017-05-13 .
- Jump up^ Delling, Daniel; Pajor, Thomas; Werneck, Renato F. (2014-10-30). “Round-Based Public Transit Routing” . Transportation Science . 49 (3): 591-604. ISSN 0041-1655 . Doi : 10.1287 / trsc.2014.0534 .