Home
India Feeds
delhi-metro
Source Data Validation Report

delhi-metro — Source Data Validation Report

This report was generated by the Canonical GTFS Schedule validator, version 7.1.0 at 2026-04-17T18:06:03Z,
for the dataset file:///shared/delhi-metro_402ab937.zip. No country code was provided.

Use this report alongside our documentation.

Summary

Agencies included

Delhi Metro Rail Corporation
- website: http://www.delhimetrorail.com/
- phone number:
- email: Not provided

Feed Info

Publisher Name:
N/A: Publisher URL:
N/A: Feed Email:
N/A: Feed Language:
N/A: Service Window:
2019-01-01 to 2025-12-31 (?) The range of service dates covered by the feed, based on trips with an associated service_id in calendar.txt and/or calendar_dates.txt

Files included

agency.txt
calendar.txt
routes.txt
shapes.txt
stop_times.txt
stops.txt
trips.txt

Counts

Agencies: 1
Blocks: 0
Routes: 36
Shapes: 36
Stops: 262
Trips: 5438

GTFS Features included (?) GTFS features provide a standardized vocabulary to define and describe features that are officially adopted in GTFS.

Shapes Trips Wheelchair Accessibility Bike Allowed

Specification Compliance report

1669 notices reported (558 errors, 1111 warnings, 0 infos)

Notice Code Severity Total

decreasing_or_equal_stop_time_distance ERROR 558

decreasing_or_equal_stop_time_distance

Decreasing or equal shape_dist_traveled in stop_times.txt.

When sorted by stop_times.stop_sequence, two consecutive entries in stop_times.txt should have increasing distance, based on the field shape_dist_traveled. If the values are equal, this is considered as an error.

You can see more about this notice here.

Only the first 50 of 558 affected records are displayed below.

tripId (?) The id of the faulty trip.	stopId (?) The id of the faulty stop.	csvRowNumber (?) The row number from `stop_times.txt`.	shapeDistTraveled (?) Actual distance traveled along the shape from the first shape point to the faulty record.	stopSequence (?) The faulty record's `stop_times.stop_sequence`.	prevCsvRowNumber (?) The row number from `stop_times.txt` of the previous stop time.	prevShapeDistTraveled (?) Actual distance traveled along the shape from the first shape point to the previous stop time.	prevStopSequence (?) The previous record's `stop_times.stop_sequence`.
"5487"	"205"	43105	22752.91	14	43104	22752.91	13
"5488"	"205"	43143	22752.91	14	43142	22752.91	13
"5492"	"205"	43295	22752.91	14	43294	22752.91	13
"5493"	"205"	43333	22752.91	14	43332	22752.91	13
"5494"	"205"	43371	22752.91	14	43370	22752.91	13
"5495"	"205"	43409	22752.91	14	43408	22752.91	13
"5496"	"205"	43447	22752.91	14	43446	22752.91	13
"5497"	"205"	43485	22752.91	14	43484	22752.91	13
"5498"	"205"	43523	22752.91	14	43522	22752.91	13
"5499"	"205"	43561	22752.91	14	43560	22752.91	13
"5490"	"205"	43219	22752.91	14	43218	22752.91	13
"5491"	"205"	43257	22752.91	14	43256	22752.91	13
"5489"	"205"	43181	22752.91	14	43180	22752.91	13
"7200"	"148"	56303	6433.368	6	56302	6433.368	5
"7202"	"148"	56327	6433.368	6	56326	6433.368	5
"7201"	"148"	56315	6433.368	6	56314	6433.368	5
"7204"	"148"	56351	6433.368	6	56350	6433.368	5
"7203"	"148"	56339	6433.368	6	56338	6433.368	5
"7211"	"148"	56435	6433.368	6	56434	6433.368	5
"7210"	"148"	56423	6433.368	6	56422	6433.368	5
"7213"	"148"	56459	6433.368	6	56458	6433.368	5
"7212"	"148"	56447	6433.368	6	56446	6433.368	5
"7215"	"148"	56483	6433.368	6	56482	6433.368	5
"7214"	"148"	56471	6433.368	6	56470	6433.368	5
"7206"	"148"	56375	6433.368	6	56374	6433.368	5
"7205"	"148"	56363	6433.368	6	56362	6433.368	5
"7208"	"148"	56399	6433.368	6	56398	6433.368	5
"7207"	"148"	56387	6433.368	6	56386	6433.368	5
"7209"	"148"	56411	6433.368	6	56410	6433.368	5
"7220"	"148"	56543	6433.368	6	56542	6433.368	5
"7222"	"148"	56567	6433.368	6	56566	6433.368	5
"7221"	"148"	56555	6433.368	6	56554	6433.368	5
"7224"	"148"	56591	6433.368	6	56590	6433.368	5
"7223"	"148"	56579	6433.368	6	56578	6433.368	5
"7226"	"148"	56615	6433.368	6	56614	6433.368	5
"7225"	"148"	56603	6433.368	6	56602	6433.368	5
"7217"	"148"	56507	6433.368	6	56506	6433.368	5
"7216"	"148"	56495	6433.368	6	56494	6433.368	5
"7219"	"148"	56531	6433.368	6	56530	6433.368	5
"7218"	"148"	56519	6433.368	6	56518	6433.368	5
"7231"	"148"	56675	6433.368	6	56674	6433.368	5
"7230"	"148"	56663	6433.368	6	56662	6433.368	5
"7233"	"148"	56699	6433.368	6	56698	6433.368	5
"7232"	"148"	56687	6433.368	6	56686	6433.368	5
"7235"	"148"	56723	6433.368	6	56722	6433.368	5
"7234"	"148"	56711	6433.368	6	56710	6433.368	5
"7237"	"148"	56747	6433.368	6	56746	6433.368	5
"7236"	"148"	56735	6433.368	6	56734	6433.368	5
"7228"	"148"	56639	6433.368	6	56638	6433.368	5
"7227"	"148"	56627	6433.368	6	56626	6433.368	5

equal_shape_distance_same_coordinates WARNING 761

equal_shape_distance_same_coordinates

Two consecutive points have equal shape_dist_traveled and the same lat/lon coordinates in shapes.txt.

When sorted by shape.shape_pt_sequence, the values for shape_dist_traveled must increase along a shape. Two consecutive points with equal values for shape_dist_traveled and the same coordinates indicate a duplicative shape point.

You can see more about this notice here.

Only the first 50 of 761 affected records are displayed below.

shapeId (?) The id of the faulty shape.	csvRowNumber (?) The row number from `shapes.txt`.	shapeDistTraveled (?) Actual distance traveled along the shape from the first shape point to the faulty record.	shapePtSequence (?) The faulty record's `shapes.shape_pt_sequence`.	prevCsvRowNumber (?) The row number from `shapes.txt` of the previous shape point.	prevShapeDistTraveled (?) Actual distance traveled along the shape from the first shape point to the previous shape point.	prevShapePtSequence (?) The previous record's `shapes.shape_pt_sequence`.
"shp_1_30"	4419	1202.405	3	4418	1202.405	2
"shp_1_30"	4421	2480.75	5	4420	2480.75	4
"shp_1_30"	4425	3314.936	9	4424	3314.936	8
"shp_1_30"	4431	4300.216	15	4430	4300.216	14
"shp_1_30"	4447	5571.618	31	4446	5571.618	30
"shp_1_30"	4471	7050.805	55	4470	7050.805	54
"shp_1_30"	4473	7784.659	57	4472	7784.659	56
"shp_1_30"	4475	9026.197	59	4474	9026.197	58
"shp_1_30"	4483	10235.563	67	4482	10235.563	66
"shp_1_30"	4490	11759.314	74	4489	11759.314	73
"shp_1_30"	4500	12810.503	84	4499	12810.503	83
"shp_1_30"	4512	13746.182	96	4511	13746.182	95
"shp_1_30"	4526	14897.064	110	4525	14897.064	109
"shp_1_30"	4551	17251.309	135	4550	17251.309	134
"shp_1_30"	4577	18996.408	161	4576	18996.408	160
"shp_1_30"	4588	20063.098	172	4587	20063.098	171
"shp_1_30"	4593	21000.166	177	4592	21000.166	176
"shp_1_30"	4606	22392.42	190	4605	22392.42	189
"shp_1_30"	4612	23489.09	196	4611	23489.09	195
"shp_1_31"	5286	1111.605	3	5285	1111.605	2
"shp_1_31"	5288	3889.762	5	5287	3889.762	4
"shp_1_31"	5290	5400.863	7	5289	5400.863	6
"shp_1_31"	5313	11451.982	30	5312	11451.982	29
"shp_1_31"	5328	15669.532	45	5327	15669.532	44
"shp_1_31"	5338	17091.699	55	5337	17091.699	54
"shp_1_31"	5340	18458.332	57	5339	18458.332	56
"shp_1_31"	5342	19104.512	59	5341	19104.512	58
"shp_1_31"	5344	20278.104	61	5343	20278.104	60
"shp_1_31"	5357	21561.262	74	5356	21561.262	73
"shp_1_31"	5367	22650.811	84	5366	22650.811	83
"shp_1_31"	5375	23967.287	92	5374	23967.287	91
"shp_1_31"	5377	24794.0	94	5376	24794.0	93
"shp_1_31"	5379	25536.137	96	5378	25536.137	95
"shp_1_31"	5384	26774.316	101	5383	26774.316	100
"shp_1_31"	5392	27998.609	109	5391	27998.609	108
"shp_1_31"	5397	28827.09	114	5396	28827.09	113
"shp_1_31"	5399	29357.646	116	5398	29357.646	115
"shp_1_31"	5401	30939.154	118	5400	30939.154	117
"shp_1_31"	5405	32082.172	122	5404	32082.172	121
"shp_1_31"	5411	33465.84	128	5410	33465.84	127
"shp_1_31"	5415	34258.355	132	5414	34258.355	131
"shp_1_31"	5423	35195.871	140	5422	35195.871	139
"shp_1_31"	5433	36957.246	150	5432	36957.246	149
"shp_1_31"	5441	38677.98	158	5440	38677.98	157
"shp_1_31"	5443	39754.605	160	5442	39754.605	159
"shp_1_31"	5445	41350.629	162	5444	41350.629	161
"shp_1_31"	5463	42658.676	180	5462	42658.676	179
"shp_1_31"	5473	44242.516	190	5472	44242.516	189
"shp_1_31"	5479	45577.223	196	5478	45577.223	195
"shp_1_31"	5492	48313.965	209	5491	48313.965	208

expired_calendar WARNING 3

expired_calendar

Dataset should not contain date ranges for services that have already expired.

This warning takes into account the calendar_dates.txt file as well as the calendar.txt file.

You can see more about this notice here.

csvRowNumber (?) The row of the faulty record.	serviceId (?) The service id of the faulty record.
2	"weekday"
3	"saturday"
4	"sunday"

fast_travel_between_consecutive_stops WARNING 257

fast_travel_between_consecutive_stops

A transit vehicle moves too fast between two consecutive stops.

The speed threshold depends on route type:

Route type	Description	Threshold, km/h
0	Light rail	100
1	Subway	150
2	Rail	500
3	Bus	150
4	Ferry	80
5	Cable tram	30
6	Aerial lift	50
7	Funicular	50
11	Trolleybus	150
12	Monorail	150
-	Unknown	200

You can see more about this notice here.

Only the first 50 of 257 affected records are displayed below.

tripCsvRowNumber (?) The row number of the problematic trip.	tripId (?) `trip_id` of the problematic trip.	routeId (?) `route_id` of the problematic trip.	speedKph (?) Travel speed (km/h).	distanceKm (?) Distance between stops (km).	csvRowNumber1 (?) The row number of the first stop time.	stopSequence1 (?) `stop_sequence` of the first stop.	stopId1 (?) `stop_id` of the first stop.	stopName1 (?) `stop_name` of the first stop.	departureTime1 (?) `departure_time` of the first stop.	csvRowNumber2 (?) The row number of the second stop time.	stopSequence2 (?) `stop_sequence` of the second stop.	stopId2 (?) `stop_id` of the second stop.	stopName2 (?) `stop_name` of the second stop.	arrivalTime2 (?) `arrival_time` of the second stop.
74	"1009"	"2"	157.89207288155598	5.833234914790818	5251	32	"40"	"Adarsh Nagar"	"20:34:19"	5252	33	"39"	"Jahangirpuri"	"20:36:32"
173	"10265"	"20"	157.89207288155598	5.833234914790818	69681	3	"39"	"Jahangirpuri"	"12:52:17"	69682	4	"40"	"Adarsh Nagar"	"12:54:30"
118	"10215"	"20"	157.89207288155598	5.833234914790818	67831	3	"39"	"Jahangirpuri"	"08:40:01"	67832	4	"40"	"Adarsh Nagar"	"08:42:14"
269	"10351"	"20"	157.89207288155598	5.833234914790818	72863	3	"39"	"Jahangirpuri"	"20:32:33"	72864	4	"40"	"Adarsh Nagar"	"20:34:46"
280	"10361"	"20"	157.89207288155598	5.833234914790818	73233	3	"39"	"Jahangirpuri"	"21:23:29"	73234	4	"40"	"Adarsh Nagar"	"21:25:42"
191	"10281"	"20"	157.89207288155598	5.833234914790818	70273	3	"39"	"Jahangirpuri"	"14:24:01"	70274	4	"40"	"Adarsh Nagar"	"14:26:14"
270	"10352"	"20"	157.89207288155598	5.833234914790818	72900	3	"39"	"Jahangirpuri"	"20:37:29"	72901	4	"40"	"Adarsh Nagar"	"20:39:42"
61	"1005"	"2"	157.89207288155598	5.833234914790818	5103	32	"40"	"Adarsh Nagar"	"20:14:35"	5104	33	"39"	"Jahangirpuri"	"20:16:48"
156	"1025"	"2"	157.89207288155598	5.833234914790818	5843	32	"40"	"Adarsh Nagar"	"21:53:15"	5844	33	"39"	"Jahangirpuri"	"21:55:28"
126	"10222"	"20"	157.89207288155598	5.833234914790818	68090	3	"39"	"Jahangirpuri"	"09:14:33"	68091	4	"40"	"Adarsh Nagar"	"09:16:46"
264	"10347"	"20"	157.89207288155598	5.833234914790818	72715	3	"39"	"Jahangirpuri"	"20:12:49"	72716	4	"40"	"Adarsh Nagar"	"20:15:02"
205	"10294"	"20"	157.89207288155598	5.833234914790818	70754	3	"39"	"Jahangirpuri"	"15:38:33"	70755	4	"40"	"Adarsh Nagar"	"15:40:46"
320	"1053"	"2"	157.89207288155598	5.833234914790818	6879	32	"40"	"Adarsh Nagar"	"24:28:11"	6880	33	"39"	"Jahangirpuri"	"24:30:24"
182	"10273"	"20"	157.89207288155598	5.833234914790818	69977	3	"39"	"Jahangirpuri"	"13:38:09"	69978	4	"40"	"Adarsh Nagar"	"13:40:22"
179	"10270"	"20"	157.89207288155598	5.833234914790818	69866	3	"39"	"Jahangirpuri"	"13:20:57"	69867	4	"40"	"Adarsh Nagar"	"13:23:10"
80	"1014"	"2"	157.89207288155598	5.833234914790818	5436	32	"40"	"Adarsh Nagar"	"20:58:59"	5437	33	"39"	"Jahangirpuri"	"21:01:12"
102	"10200"	"20"	157.89207288155598	5.833234914790818	67276	3	"39"	"Jahangirpuri"	"07:18:49"	67277	4	"40"	"Adarsh Nagar"	"07:21:02"
287	"10368"	"20"	157.89207288155598	5.833234914790818	73492	3	"39"	"Jahangirpuri"	"22:03:37"	73493	4	"40"	"Adarsh Nagar"	"22:05:50"
121	"10218"	"20"	157.89207288155598	5.833234914790818	67942	3	"39"	"Jahangirpuri"	"08:54:49"	67943	4	"40"	"Adarsh Nagar"	"08:57:02"
159	"10252"	"20"	157.89207288155598	5.833234914790818	69200	3	"39"	"Jahangirpuri"	"11:42:33"	69201	4	"40"	"Adarsh Nagar"	"11:44:46"
17	"1001"	"2"	157.89207288155598	5.833234914790818	4955	32	"40"	"Adarsh Nagar"	"19:54:51"	4956	33	"39"	"Jahangirpuri"	"19:57:04"
244	"10329"	"20"	157.89207288155598	5.833234914790818	72049	3	"39"	"Jahangirpuri"	"18:44:01"	72050	4	"40"	"Adarsh Nagar"	"18:46:14"
248	"10332"	"20"	157.89207288155598	5.833234914790818	72160	3	"39"	"Jahangirpuri"	"18:58:49"	72161	4	"40"	"Adarsh Nagar"	"19:01:02"
271	"10353"	"20"	157.89207288155598	5.833234914790818	72937	3	"39"	"Jahangirpuri"	"20:42:25"	72938	4	"40"	"Adarsh Nagar"	"20:44:38"
129	"10225"	"20"	157.89207288155598	5.833234914790818	68201	3	"39"	"Jahangirpuri"	"09:29:21"	68202	4	"40"	"Adarsh Nagar"	"09:31:34"
284	"10365"	"20"	157.89207288155598	5.833234914790818	73381	3	"39"	"Jahangirpuri"	"21:46:25"	73382	4	"40"	"Adarsh Nagar"	"21:48:38"
197	"10287"	"20"	157.89207288155598	5.833234914790818	70495	3	"39"	"Jahangirpuri"	"14:58:25"	70496	4	"40"	"Adarsh Nagar"	"15:00:38"
216	"10303"	"20"	157.89207288155598	5.833234914790818	71087	3	"39"	"Jahangirpuri"	"16:30:09"	71088	4	"40"	"Adarsh Nagar"	"16:32:22"
259	"10342"	"20"	157.89207288155598	5.833234914790818	72530	3	"39"	"Jahangirpuri"	"19:48:09"	72531	4	"40"	"Adarsh Nagar"	"19:50:22"
235	"10320"	"20"	157.89207288155598	5.833234914790818	71716	3	"39"	"Jahangirpuri"	"17:59:37"	71717	4	"40"	"Adarsh Nagar"	"18:01:50"
283	"10364"	"20"	157.89207288155598	5.833234914790818	73344	3	"39"	"Jahangirpuri"	"21:40:41"	73345	4	"40"	"Adarsh Nagar"	"21:42:54"
164	"10257"	"20"	157.89207288155598	5.833234914790818	69385	3	"39"	"Jahangirpuri"	"12:07:13"	69386	4	"40"	"Adarsh Nagar"	"12:09:26"
246	"10330"	"20"	157.89207288155598	5.833234914790818	72086	3	"39"	"Jahangirpuri"	"18:48:57"	72087	4	"40"	"Adarsh Nagar"	"18:51:10"
178	"1027"	"2"	157.89207288155598	5.833234914790818	5917	32	"40"	"Adarsh Nagar"	"22:03:07"	5918	33	"39"	"Jahangirpuri"	"22:05:20"
169	"10261"	"20"	157.89207288155598	5.833234914790818	69533	3	"39"	"Jahangirpuri"	"12:29:21"	69534	4	"40"	"Adarsh Nagar"	"12:31:34"
150	"10244"	"20"	157.89207288155598	5.833234914790818	68904	3	"39"	"Jahangirpuri"	"11:03:05"	68905	4	"40"	"Adarsh Nagar"	"11:05:18"
171	"10263"	"20"	157.89207288155598	5.833234914790818	69607	3	"39"	"Jahangirpuri"	"12:40:49"	69608	4	"40"	"Adarsh Nagar"	"12:43:02"
78	"1012"	"2"	157.89207288155598	5.833234914790818	5362	32	"40"	"Adarsh Nagar"	"20:49:07"	5363	33	"39"	"Jahangirpuri"	"20:51:20"
127	"10223"	"20"	157.89207288155598	5.833234914790818	68127	3	"39"	"Jahangirpuri"	"09:19:29"	68128	4	"40"	"Adarsh Nagar"	"09:21:42"
242	"10327"	"20"	157.89207288155598	5.833234914790818	71975	3	"39"	"Jahangirpuri"	"18:34:09"	71976	4	"40"	"Adarsh Nagar"	"18:36:22"
180	"10271"	"20"	157.89207288155598	5.833234914790818	69903	3	"39"	"Jahangirpuri"	"13:26:41"	69904	4	"40"	"Adarsh Nagar"	"13:28:54"
99	"10199"	"20"	157.89207288155598	5.833234914790818	67239	3	"39"	"Jahangirpuri"	"07:13:05"	67240	4	"40"	"Adarsh Nagar"	"07:15:18"
139	"10234"	"20"	157.89207288155598	5.833234914790818	68534	3	"39"	"Jahangirpuri"	"10:13:45"	68535	4	"40"	"Adarsh Nagar"	"10:15:58"
149	"10243"	"20"	157.89207288155598	5.833234914790818	68867	3	"39"	"Jahangirpuri"	"10:58:09"	68868	4	"40"	"Adarsh Nagar"	"11:00:22"
138	"10233"	"20"	157.89207288155598	5.833234914790818	68497	3	"39"	"Jahangirpuri"	"10:08:49"	68498	4	"40"	"Adarsh Nagar"	"10:11:02"
92	"10192"	"20"	157.89207288155598	5.833234914790818	66980	3	"39"	"Jahangirpuri"	"06:32:57"	66981	4	"40"	"Adarsh Nagar"	"06:35:10"
71	"1006"	"2"	157.89207288155598	5.833234914790818	5140	32	"40"	"Adarsh Nagar"	"20:19:31"	5141	33	"39"	"Jahangirpuri"	"20:21:44"
225	"10311"	"20"	157.89207288155598	5.833234914790818	71383	3	"39"	"Jahangirpuri"	"17:15:13"	71384	4	"40"	"Adarsh Nagar"	"17:17:26"
194	"10284"	"20"	157.89207288155598	5.833234914790818	70384	3	"39"	"Jahangirpuri"	"14:41:13"	70385	4	"40"	"Adarsh Nagar"	"14:43:26"
144	"10239"	"20"	157.89207288155598	5.833234914790818	68719	3	"39"	"Jahangirpuri"	"10:38:25"	68720	4	"40"	"Adarsh Nagar"	"10:40:38"

missing_recommended_field WARNING 36

missing_recommended_field

A recommended field is missing.

The given field has no value in some input row, even though values are recommended.

You can see more about this notice here.

filename (?) The name of the faulty file.	csvRowNumber (?) The row of the faulty record.	fieldName (?) The name of the missing field.
"routes.txt"	2	"agency_id"
"routes.txt"	3	"agency_id"
"routes.txt"	4	"agency_id"
"routes.txt"	5	"agency_id"
"routes.txt"	6	"agency_id"
"routes.txt"	7	"agency_id"
"routes.txt"	8	"agency_id"
"routes.txt"	9	"agency_id"
"routes.txt"	10	"agency_id"
"routes.txt"	11	"agency_id"
"routes.txt"	12	"agency_id"
"routes.txt"	13	"agency_id"
"routes.txt"	14	"agency_id"
"routes.txt"	15	"agency_id"
"routes.txt"	16	"agency_id"
"routes.txt"	17	"agency_id"
"routes.txt"	18	"agency_id"
"routes.txt"	19	"agency_id"
"routes.txt"	20	"agency_id"
"routes.txt"	21	"agency_id"
"routes.txt"	22	"agency_id"
"routes.txt"	23	"agency_id"
"routes.txt"	24	"agency_id"
"routes.txt"	25	"agency_id"
"routes.txt"	26	"agency_id"
"routes.txt"	27	"agency_id"
"routes.txt"	28	"agency_id"
"routes.txt"	29	"agency_id"
"routes.txt"	30	"agency_id"
"routes.txt"	31	"agency_id"
"routes.txt"	32	"agency_id"
"routes.txt"	33	"agency_id"
"routes.txt"	34	"agency_id"
"routes.txt"	35	"agency_id"
"routes.txt"	36	"agency_id"
"routes.txt"	37	"agency_id"

missing_recommended_file WARNING 1

missing_recommended_file

A recommended file is missing.

You can see more about this notice here.

filename (?) The name of the faulty file.
"feed_info.txt"

stop_too_far_from_shape WARNING 26

stop_too_far_from_shape

Stop too far from trip shape.

Per GTFS Best Practices, route alignments (in shapes.txt) should be within 100 meters of stop locations which a trip serves. This potentially indicates a problem with the location of the stop or the path of the shape.

You can see more about this notice here.

tripCsvRowNumber (?) The row number of the faulty record from `trips.txt`.	shapeId (?) The id of the shape that is referred to.	tripId (?) The id of the trip that is referred to.	stopTimeCsvRowNumber (?) The row number of the faulty record from `stop_times.txt`.	stopId (?) The id of the stop that is referred to.	stopName (?) The name of the stop that is referred to.	match (?) Latitude and longitude pair of the location.	geoDistanceToShape (?) Distance from stop to shape.
85	"shp_1_31"	"10186"	66771	"51"	"Patel Chowk"	[28.622280703850837,77.21348172400208]	139.2817377989638
85	"shp_1_31"	"10186"	66774	"54"	"Lok Kalyan Marg"	[28.59762521833573,77.21104694471687]	188.30247695406717
85	"shp_1_31"	"10186"	66780	"60"	"Malviya Nagar"	[28.528691982012838,77.2045611172752]	117.93602269387935
3379	"shp_1_12"	"3611"	23456	"72"	"Vaishali"	[28.64900602691835,77.33842184366443]	141.47755259347156
329	"shp_1_34"	"10736"	74052	"51"	"Patel Chowk"	[28.622280703850837,77.21348172400208]	139.2817377989638
329	"shp_1_34"	"10736"	74055	"54"	"Lok Kalyan Marg"	[28.59762521833573,77.21104694471687]	188.30247695406717
329	"shp_1_34"	"10736"	74061	"60"	"Malviya Nagar"	[28.528691982012838,77.2045611172752]	117.93602269387935
6	"shp_1_13"	"1000"	4897	"60"	"Malviya Nagar"	[28.528691982012838,77.2045611172752]	117.93602269387935
6	"shp_1_13"	"1000"	4903	"54"	"Lok Kalyan Marg"	[28.59762521833573,77.21104694471687]	188.30247695406717
6	"shp_1_13"	"1000"	4906	"51"	"Patel Chowk"	[28.622280703850837,77.21348172400208]	139.2817377989638
1713	"shp_1_35"	"14810"	110076	"215"	"Maujpur - Babarpur"	[28.692444988036605,77.27991469255275]	107.30881951276864
1713	"shp_1_35"	"14810"	110088	"224"	"Mayur Vihar Pocket 1"	[28.605270294146873,77.29684516579553]	156.0915498131951
1713	"shp_1_35"	"14810"	110089	"87"	"Mayur Vihar-I"	[28.603247369028953,77.29001593029356]	143.24900554593302
990	"shp_1_36"	"12942"	90414	"72"	"Vaishali"	[28.64900602691835,77.33842184366443]	141.47755259347156
2276	"shp_1_18"	"16107"	121673	"157"	"Shivaji Stadium"	[28.62818730614857,77.2096916011387]	102.41865558638997
4561	"shp_1_2"	"6564"	54457	"113"	"Dwarka"	[28.615887,77.022461]	111.07796279288127
4637	"shp_1_7"	"6792"	54765	"157"	"Shivaji Stadium"	[28.62818730614857,77.2096916011387]	102.41865558638997
2176	"shp_1_1"	"15876"	121367	"113"	"Dwarka"	[28.615887,77.022461]	111.07796279288127
2424	"shp_1_24"	"16449"	122362	"68"	"Sikanderpur"	[28.48106546602949,77.09443808222429]	144.59721423423764
1500	"shp_1_25"	"1420"	7108	"60"	"Malviya Nagar"	[28.528691982012838,77.2045611172752]	117.93602269387935
1500	"shp_1_25"	"1420"	7114	"54"	"Lok Kalyan Marg"	[28.59762521833573,77.21104694471687]	188.30247695406717
1500	"shp_1_25"	"1420"	7117	"51"	"Patel Chowk"	[28.622280703850837,77.21348172400208]	139.2817377989638
4161	"shp_1_26"	"5487"	43112	"87"	"Mayur Vihar-I"	[28.603247369028953,77.29001593029356]	143.24900554593302
4161	"shp_1_26"	"5487"	43113	"224"	"Mayur Vihar Pocket 1"	[28.605270294146873,77.29684516579553]	156.0915498131951
4161	"shp_1_26"	"5487"	43125	"215"	"Maujpur - Babarpur"	[28.692466864472742,77.27985155329263]	113.92610928445382
4749	"shp_1_29"	"7128"	55438	"68"	"Sikanderpur"	[28.48106546602949,77.09443808222429]	144.59721423423764

stop_too_far_from_shape_using_user_distance WARNING 26

stop_too_far_from_shape_using_user_distance

Stop time too far from shape.

A stop time entry that is a large distance away from the location of the shape in shapes.txt as defined by shape_dist_traveled values.

You can see more about this notice here.

tripCsvRowNumber (?) The row number of the faulty record from `trips.txt`.	shapeId (?) The id of the shape that is referred to.	tripId (?) The id of the trip that is referred to.	stopTimeCsvRowNumber (?) The row number of the faulty record from `stop_times.txt`.	stopId (?) The id of the stop that is referred to.	stopName (?) The name of the stop that is referred to.	match (?) Latitude and longitude pair of the location.	geoDistanceToShape (?) Distance from stop to shape.
2	"shp_1_30"	"0"	13	"10"	"Pul Bangash"	[28.666376000000007,77.20742]	129.67155273542855
85	"shp_1_31"	"10186"	66772	"52"	"Central Secretariat"	[28.615882999999997,77.21225]	103.46154321595682
85	"shp_1_31"	"10186"	66779	"59"	"Hauz Khas"	[28.544262,77.20668]	101.85700770373853
3669	"shp_1_32"	"430"	286	"10"	"Pul Bangash"	[28.666376000000007,77.20742]	129.67155273542855
7	"shp_1_33"	"10000"	65061	"10"	"Pul Bangash"	[28.666376000000007,77.20742]	129.67155273542855
329	"shp_1_34"	"10736"	74053	"52"	"Central Secretariat"	[28.615882999999997,77.21225]	103.46154321595682
329	"shp_1_34"	"10736"	74060	"59"	"Hauz Khas"	[28.544262,77.20668]	101.85700770373853
6	"shp_1_13"	"1000"	4898	"59"	"Hauz Khas"	[28.544262,77.20668]	101.85700770373853
6	"shp_1_13"	"1000"	4905	"52"	"Central Secretariat"	[28.615882999999997,77.21225]	103.46154321595682
1713	"shp_1_35"	"14810"	110082	"75"	"Karkarduma"	[28.649284000000005,77.305969]	152.5384659974888
3824	"shp_1_15"	"4761"	35526	"122"	"ITO"	[28.628191000000005,77.241074]	110.28308286640508
3824	"shp_1_15"	"4761"	35529	"52"	"Central Secretariat"	[28.615870000000005,77.212364]	104.96562573004756
3824	"shp_1_15"	"4761"	35530	"124"	"Khan Market"	[28.602264,77.229141]	112.10740248853288
1387	"shp_1_5"	"14087"	102552	"124"	"Khan Market"	[28.602224000000003,77.229111]	111.41061608202256
1387	"shp_1_5"	"14087"	102553	"52"	"Central Secretariat"	[28.615877000000005,77.212303]	104.01775147790157
1387	"shp_1_5"	"14087"	102556	"122"	"ITO"	[28.628195000000005,77.24101300000001]	110.24403162866713
3991	"shp_1_19"	"5121"	38539	"122"	"ITO"	[28.628191000000005,77.241074]	110.28308286640508
3991	"shp_1_19"	"5121"	38542	"52"	"Central Secretariat"	[28.615870000000005,77.212364]	104.96562573004756
3991	"shp_1_19"	"5121"	38543	"124"	"Khan Market"	[28.602264,77.229141]	112.10740248853288
5281	"shp_1_6"	"9333"	61714	"10"	"Pul Bangash"	[28.666376000000007,77.20742]	129.67155273542855
1500	"shp_1_25"	"1420"	7109	"59"	"Hauz Khas"	[28.544262,77.20668]	101.85700770373853
1500	"shp_1_25"	"1420"	7116	"52"	"Central Secretariat"	[28.615882999999997,77.21225]	103.46154321595682
4161	"shp_1_26"	"5487"	43119	"75"	"Karkarduma"	[28.649284000000005,77.305969]	152.5384659974888
1543	"shp_1_27"	"14446"	105576	"124"	"Khan Market"	[28.602224000000003,77.229111]	111.41061608202256
1543	"shp_1_27"	"14446"	105577	"52"	"Central Secretariat"	[28.615877000000005,77.212303]	104.01775147790157
1543	"shp_1_27"	"14446"	105580	"122"	"ITO"	[28.628195000000005,77.24101300000001]	110.24403162866713

trip_coverage_not_active_for_next7_days WARNING 1

trip_coverage_not_active_for_next7_days

Trips data should be valid for at least the next seven days.

This notice is triggered if the date range where a significant number of trips are running ends in less than 7 days.

You can see more about this notice here.

currentDate (?) Current date (YYYYMMDD format).	serviceWindowStartDate (?) The start date of the majority service window.	serviceWindowEndDate (?) The end date of the majority service window.
"20260417"	"20190101"	"20251231"