Quickstart

Requirements

• A 64-bit operating system. Linux, MacOS and Windows are currently supported.

• Python version 3.7 or higher

• Python Modules: tqdm, pandas, numpy, pickle, networkx

Running Test cases

1. Clone the repository using following command:

   git clone https://github.com/transnetlab/transit-routing.git
   

2. Enter following commands. In case of error, edit main.py

   cd transit-routing
   pip install -r requirements.txt
   python main.py
   

3. Enter following parameters when prompted:

   Network name: anaheim
   Date: 20220630
   Bus routes: 3, -1
   build transfers file: 0
   build TBTR files: 0
   build CSA files: 0
   build Transfer Pattern files: 0
   build Time Expanded files: 0
   use test case: 1
   RAPTOR environment: 0
   Standard RAPTOR: 0
   

4. Following output should be displayed:

   ___________________Query Parameters__________________
   Network: ./anaheim
   SOURCE stop id: 36
   DESTINATION stop id: 52
   Maximum Transfer allowed: 4
   Is walking from SOURCE allowed ?: 1
   Earliest departure time: 2022-06-30 05:41:00
   _____________________________________________________
   from 36 walk till  38.0 for 337.2 seconds
   from 38 board at 06:38:18 and get down on 1 at 06:41:53 along 1020_0
   from 1 walk till  17.0 for 171.7 seconds
   from 17 board at 06:53:44 and get down on 30 at 06:55:53 along 1025_1
   from 30 walk till  53.0 for 124.7 seconds
   from 53 board at 07:14:00 and get down on 68 at 07:20:00 along 1018_2
   from 68 board at 15:00:00 and get down on 52 at 15:12:00 along 1030_0
   ####################################
   from 36 walk till  38.0 for 337.2 seconds
   from 38 board at 06:38:18 and get down on 1 at 06:41:53 along 1020_0
   from 1 board at 18:30:00 and get down on 35 at 18:31:50 along 1014_0
   from 35 walk till  51.0 for 85.2 seconds
   from 51 board at 18:37:00 and get down on 52 at 18:42:00 along 1012_0
   ####################################
   Optimal arrival time are: [[Timestamp('2022-06-30 15:12:00'), Timestamp('2022-06-30 18:42:00')]]
   

Running your instance

Due to inconsistencies in the GTFS sets available online, they cannot be used directly. A significant amount of preprocessing (e.g., transitively closed footpaths, non-overtaking trips, continuous stop sequence) is required. GTFS_wrapper.py provides the functions to convert GTFS set into required format. See GTFS preprocessing for further details.

Rename your GTFS.zip to network_GTFS.zip (e.g., anaheim_GTFS.zip) and place it in main directory. Run main.py and enter the variables accordingly.

Once the GTFS set is processed successfully, directly run query_file.py to find optiaml journeys.

Additional notes

• To print complete itinerary, set PRINT_ITINERARY = 1 and OPTIMIZED = 0.

• Additional target pruning is applied in the footpath phase of all RAPTOR related algorithms.

• To compare the output of RAPTOR and TBTR, WALKING_FROM_SOURCE must be set to 1.

• Post processing in rRAPTOR and rTBTR gives the set of optimal trips (or routes id **OPTIMIZED**=0) required to cover all optimal journeys. However, the output set by rRAPTOR and rTBTR might not match. To understand this, imagine two different journeys with the same arrival times and number of transfers. Since, all algorithms are coded using strict dominance, only one of the two will be detected. While the rRAPTOR might include the first journey, rTBTR can include second. In such cases, algorithm’s correctness can be checked by comparing the optimal arrival times (which should be same).