Table 4 Continuation of the sparsity of the currently provided Uber travel time datasets in percentage. Cities starting with N – W. The variable N indicates the total number of city zone polygons in which the respective city is divided.

From: City-scale car traffic and parking density maps from Uber Movement travel time data

		2015				2016				2017				2018
		Q1	Q2	Q3	Q4	Q1	Q2	Q3	Q4	Q1	Q2	Q3	Q4	Q1	Q2	Q3	Q4
Nairobi N = 400	All	—	—	—	—	94	92	86	80	79	80	79	78	78	76	77	74
	WD	—	—	—	—	96	93	89	83	83	83	83	81	81	80	81	78
	WE	—	—	—	—	97	95	91	86	86	87	85	85	85	84	84	82
New Delhi N = 290	All	—	—	—	—	48	47	39	30	34	34	36	32	33	29	29	28
	WD	—	—	—	—	53	52	44	35	39	39	41	37	38	34	34	32
	WE	—	—	—	—	62	62	54	45	49	48	49	46	48	42	41	42
Orlando N = 1,893	All	—	—	—	—	94	94	93	92	91	91	92	90	91	92	92	91
	WD	—	—	—	—	95	95	94	94	93	93	93	92	93	93	94	93
	WE	—	—	—	—	97	97	97	96	96	96	96	95	96	96	96	96
Paris N = 5,260	All	—	—	—	—	96	95	94	93	93	92	92	92	95	92	93	93
	WD	—	—	—	—	96	96	95	94	94	94	94	94	95	93	94	94
	WE	—	—	—	—	97	97	97	96	96	96	95	95	95	95	96	96
Perth N = 173	All	—	—	—	—	73	70	67	62	63	63	62	58	60	62	60	57
	WD	—	—	—	—	79	77	74	69	70	70	69	66	67	68	67	64
	WE	—	—	—	—	80	77	75	70	72	71	69	67	69	70	68	67
Pittsburgh N = 608	All	—	—	—	—	93	91	91	90	90	89	89	88	88	88	86	86
	WD	—	—	—	—	94	93	93	92	92	91	91	90	90	90	89	89
	WE	—	—	—	—	96	95	94	94	94	93	93	92	93	92	91	92
San Francisco N = 2,710	All	—	—	—	—	96	96	95	95	95	95	94	94	94	94	94	94
	WD	—	—	—	—	97	96	96	96	96	95	95	95	95	95	94	94
	WE	—	—	—	—	98	97	97	97	97	97	96	96	96	96	96	96
Santiago N = 866	All	—	—	—	—	94	86	73	59	58	57	56	54	57	59	59	59
	WD	—	—	—	—	95	90	78	66	65	65	63	62	63	67	66	66
	WE	—	—	—	—	97	92	83	70	70	69	67	66	69	69	70	71
Sao Paulo N = 517	All	—	—	—	—	82	74	67	61	60	58	58	56	56	56	55	54
	WD	—	—	—	—	85	78	72	65	64	63	64	61	61	61	60	58
	WE	—	—	—	—	88	82	76	71	70	69	68	67	68	67	66	67
Seattle N = 776	All	—	—	—	—	94	93	92	91	91	91	90	90	90	90	89	90
	WD	—	—	—	—	95	94	93	93	93	92	92	92	92	91	91	91
	WE	—	—	—	—	96	95	95	94	94	94	93	93	94	93	93	93
Stockholm N = 776	All	—	—	—	—	96	93	94	93	93	92	92	91	92	91	90	90
	WD	—	—	—	—	95	95	96	95	95	94	94	94	94	93	93	92
	WE	—	—	—	—	95	95	97	96	96	95	95	94	95	94	94	94
Sydney N = 3,639	All	98	98	97	96	95	95	94	92	92	92	92	91	92	92	91	91
	WD	98	98	98	97	96	96	95	94	94	94	94	93	93	94	93	92
	WE	99	99	98	97	97	96	96	95	95	95	94	94	94	94	94	94
Taipei N = 691	All	—	—	—	—	76	73	70	67	78	84	78	75	74	71	70	69
	WD	—	—	—	—	79	77	74	71	81	86	81	79	77	75	74	73
	WE	—	—	—	—	85	83	80	77	88	92	86	84	83	81	79	79
Tampa Bay N = 503	All	—	—	—	—	82	81	80	78	75	75	77	75	75	77	77	77
	WD	—	—	—	—	85	84	83	82	80	80	81	79	79	81	81	80
	WE	—	—	—	—	89	88	87	85	84	84	85	83	83	85	85	85
Toronto N = 141	All	—	—	—	—	27	22	20	19	20	16	15	16	17	16	13	13
	WD	—	—	—	—	34	30	26	26	27	22	22	22	23	22	19	19
	WE	—	—	—	—	42	35	33	30	33	27	26	27	31	27	24	25
Washington D.C. N = 558	All	—	—	—	—	62	58	54	53	53	53	51	48	46	44	46	47
	WD	—	—	—	—	67	64	60	59	59	59	57	55	52	51	52	53
	WE	—	—	—	—	74	70	68	66	67	66	64	62	61	58	60	62
West Midlands UK N = 806	All	—	—	—	—	98	98	98	97	96	95	95	95	95	95	94	94
	WD	—	—	—	—	97	96	95	94	92	90	89	89	89	96	96	95
	WE	—	—	—	—	97	97	96	94	93	92	90	90	91	96	96	96

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Table 4 Continuation of the sparsity of the currently provided Uber travel time datasets in percentage. Cities starting with N – W. The variable N indicates the total number of city zone polygons in which the respective city is divided.

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