Development of Pavement Distress Deterioration Prediction Models for Urban Road Network Using Genetic Programming

Table 2

Traffic volume data of road sections.

Section ID	Cycle	Rickshaw/rehri	Scooter/M-cycle	Car/jeep/auto	Bus/truck/tractor/trolly	Cart	Total	ESA in the year 2013 (in millions)
UR01	198	118	350	371	112	22	1171	0.281
	16.9	10	30	31.7	9.6	1.8	100
UR02	234	73	552	285	33	5	1182	0.072
	19.8	6.2	46.7	24.1	2.8	0.4	100
UR03	152	141	341	285	78	5	1002	0.112
	15.2	14.07	34.03	28.44	7.8	0.5	100
UR04	108	18	251	115	10	3	505	0.194
	21.7	3.5	49.7	22.7	1.9	0.6	100
UR05	110	69	272	317	120	12	900	0.424
	12.2	7.66	30.2	35.2	13.3	1.44	100
UR06	251	180	547	265	33	4	1280	0.144
	20	14	42	21	2.6	0.4	100
UR07	267	201	584	535	129	15	1731	0.230
	15.4	11.6	33.7	31	7.4	0.9	100
UR08	191	207	651	880	425	3	2357	0.127
	8.1	8.8	27.61	37.34	18.03	0.12	100
UR09	171	158	470	338	29	2	1168	0.225
	15	14	40	28.4	2.5	0.1	100
UR10	407	459	1104	65	5	—	2040	0.129
	20	22.5	54.1	3.2	0.2	—	100
UR11	583	247	705	864	438	5	2842	0.166
	21	9	25	29.8	15	0.2	100
UR12	464	358	722	878	34	12	2468	0.036
	19	14.5	29	35.6	1.4	0.5	100
UR13	404	92	548	347	196	5	1592	0.902
	25.4	5.8	34.4	21.8	12.3	0.3	100
UR14	554	349	1275	1106	112	8	3404	0.115
	16.3	10.2	37.5	32.5	3.3	0.2	100
UR15	381	319	1427	1179	714	20	4040	0.928
	9.4	7.6	35.2	29.2	17.6	1	100
UR16	371	180	580	144	2	3	1280	0.003
	29	14	45.3	11.3	0.15	0.25	100