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7 Infrastructure Development Plan

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7 Infrastructure Development Plan 7 Infrastructure Development Plan 7.1 Roads and Public Transport 7.1.1 Sector Overview Major industrial linkage to/from Krishnapatnam node will be developed with Bengaluru, Chennai, Vijaywada, Guntur, Nellore, Chennai Port, Ennore Port, and Sri City and corresponding roads to the linkages are delineated as shown in Figure 6-1. Present lane numbers of the roads on linkages are four or six on linkages with Chennai, Vijaywada, Guntur, Sri City, Chennai Port, and Ennore Port. However, there are some sections which are given only two lanes on linkages with Bengaluru. Vijaywada Guntur Krishnapatnam Port Krishnapatnam Node NH5 Sri City Ennore Port NH4-NH18-NH18A-SH61-NH5 Chennai Port Source: JICA Study Team Figure 7.1: Number of Lanes on Major External Linkage (Krishnapatnam) Linear distances from Krishnapatnam nodes to major destinations which are related to industrial activities of Krishnapatnam nodes are summarized in Table 7-1. Table 7.1: Distance from Nodes From Krishnapatnam Node Chennai 136 Nellore 33 To /Linear Bengaluru 310 Chennai Port 119 Distance(km) Vijaywada 256 Ennore Port 109 Guntur 226 Sri City 77 Source: JICA Study Team Final Report - Krishnapatnam Industrial Node Development Plan PwC/Nippon Koei 154 Land demands and population demands of Krishnapatnam Node in three phases are forecasted by the JICA Study Team as shown in Table 6.5 and Table 7.2, respectively. Table 7.2: Population Demand Forecast in Krishnapatnam Node Phase-1 Phase-2 Phase-3 Working Population 97,336 243,849 582,706 Residential Population 33,408 83,695 200,000 Source: JICA Study Team Currently the Krishnapatnam port is accessed from the west by a 4 lane road. The port has acquired land between road and rail, to permit expansion of both. According to discussion result with Krishnapatnam Port Company Limited held on 26 June 2014, accesses from south is being proposed (6 lane road) running from Naidupet (NH 5) via Kota and new industrial park to Krishnapatnam Port with length of about 50 km. Development issues are selected with consideration of development vision discussed in Chapter 5.3 and envisioned future road and traffic conditions of Krishnapatnam node, such as public transport promotion, smooth transit in node, segregation of cargo traffic, and environmental conservation. Node development plan is examined based on those issues. 7.1.2 Demand Supply Analysis for External Node Infrastructures General Traffic demand from/to node is predicted to examine capacity of node access roads and rail. Commuter traffic and cargo traffic are subjected to prediction of the traffic demand. Prediction of the traffic demand and capacity examination is made for each phases. Methodology Commuter Traffic Demand Traffic prediction method for the commuter traffic is explained below: Final Report - Krishnapatnam Industrial Node Development Plan PwC/Nippon Koei 155 ① Distribute estimated node employee (except employee living in node) to neighboring Sub-District ② Identification of Node Access Point ③ Grouping of Sub-Districts by access points and estimation of total employee by access points ④ Examination of work time zones and peak hour total employee number by access point ⑤ Assumption of type and share of commuting transport modes by access point ⑥ Selection of traffic parameters (occupancy ratio, pcu convert factors, etc.) ⑦ Estimation of peak hour pcu traffic volume by traffic mode and access points Source: JICA Study Team Figure 7.2: Work Flow for Commuter Traffic Demand Estimate Distribute estimated node employee（except employee living in node） to neighboring Sub-District: Estimated node employee in foregoing chapter （ except employee living in node ） is distributed to neighbouring sub-district in considering of urbanization trend and population frame of sub-districts. Sub- districts within about 30km from node centre are selected for the distribution. Urbanization trend and the distribution result to sub-district are shown in Figure 7.3 and Table 7.3, respectively. Final Report - Krishnapatnam Industrial Node Development Plan PwC/Nippon Koei 156 Source: JICA Study Team Figure 7.3: Envisaged Direction of Urbanization Table 7.3: Distribution of Node Employee to Vicinal Sub-Districts Year Node Item Unit 2019 2024 2034 Residential Acre 187 469 1,120 Area Whole Acre 1,300 3,258 7,785 Residential person 33,398 83,699 200,000 Employee (Res.) person 7,260 18,196 43,478 Sub-Districts Muthukur person 4,739 11,877 28,380 Venkatachalam person 4,739 11,877 28,380 Manubolu person 3,475 8,710 20,812 Chillakur person 4,739 11,877 28,380 Krishnapatnam Kota person 4,739 11,877 28,380 Population Employee Ojili person 2,844 7,126 17,028 (Non-Res.) Naidupeta person 6,319 15,836 37,841 Chittamur person 3,791 9,502 22,704 Vakadu person 3,475 8,710 20,812 Thotapalligudur person 3,791 9,502 22,704 Nellore person 34,754 87,099 208,123 Gudur person 9,478 23,754 56,761 Doravarisatram person 3,159 7,918 18,920 Employee total person 97,305 243,861 582,706 Source: JICA Study Team Final Report - Krishnapatnam Industrial Node Development Plan PwC/Nippon Koei 157 Identification of node access points Four node access points (A to D) are identified based on sub-districts which are distributed node commuter, existing roads and rail, and land use plan in node as shown in Figure 7.4. Colored solid lines express commuter volume between sub-district and node. Colored dotted line expresses access route from sub-districts to access points. Source: JICA Study Team Figure 7.4: Identification of Node Access Point Grouping of sub-districts by access points and estimation of total employee by access points As hinterlands of each access points, sub-district are categorized into each access points and total employee by access points are estimated as shown in Table 7.4. Some sub-districts distribute employee to two access points due to positional property. Table 7.4: Grouping of Node Employee by Access Points Year Sub-District 2019 2024 2034 A B C D A B C D A B C D Muthukur 4,739 11,877 28,380 Venkatachalam 4,739 11,877 28,380 Manubolu 3,475 8,710 20,812 Chillakur 4,739 11,877 28,380 Final Report - Krishnapatnam Industrial Node Development Plan PwC/Nippon Koei 158 Year Sub-District 2019 2024 2034 A B C D A B C D A B C D Kota 3,159 1,580 7,918 3,959 18,920 9,460 Ojili 2,844 7,126 17,028 Naidupeta 6,319 15,836 37,841 Chittamur 3,791 9,502 22,704 Vakadu 3,475 8,710 20,812 Thotapalligudur 3,791 9,502 22,704 Nellore 6,951 27,803 17,420 69,679 41,625 166,498 Gudur 9,478 23,754 56,761 Doravarisatram 3,159 7,918 18,920 15,481 22,748 15,797 36,018 38,799 57,010 39,590 90,266 92,709 136,226 94,601 215,691 Total 90,044 225,665 539,228 Source: JICA Study Team Examination of work time zones and peak hour total employee number by access point Three work time zones in a day is assumed to introduce in node and hourly work time zones in consideration of commuting time dispersion are proposed as shown in Figure 7.5. Peak hour total employee number is estimated based on the work time zones for each access points as shown in Table 7.5. Time Working Shift 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 1 2 3 Commute time - IN-a Commute time - OUT-a Commute time - IN-b Commute time - OUT-b Source: JICA Study Team Figure 7.5: Proposed Work Time Zones Table 7.5: Number of Node Employee in Peak Hour Year 2019 2024 2034 A B C D A B C D A B C D 2,580 3,791 2,633 6,003 6,466 9,502 6,598 15,044 15,452 22,704 15,767 35,949 Source: JICA Study Team Final Report - Krishnapatnam Industrial Node Development Plan PwC/Nippon Koei 159 Assumption of type and share of commuting transport modes by access point Motor-cycle, Car(including para-transit), and Bus are selected as commuting transport mode and share of 20%, 20%, 60% are assumed as basic share of commuting transports. Mass Transit System, such as BRT, LRT, and Rail is selected, in case that traffic demand exceeds capacity of road transports. Selection of traffic parameters (occupancy ratio, pcu convert factors, etc.） Traffic parameters, such as occupancy ratio and pcu convert factors are selected in consideration of practical example in India as shown in Table 7.6. Table 7.6: Traffic Parameters Vehicle Occupancy PCU Convert Type Ratio Factor MC 1.2 0.5 Car 2.0 1.0 Bus 60 3.0 BRT 60 - LRT 1,500 - Railway 2,000 - Source: JICA Study Team Estimation of peak hour pcu traffic volume by traffic mode and access points Peak hour pcu commuter traffic volume are estimated based on peak hour commuter number, commuting transport mode, share of commuting transport mode, and traffic parameter for each access points. Freight Traffic Demand Traffic prediction method for the cargo traffic is explained below: ① Identification of input materials and output products by industry, Assumption of origins and destinations, packing type, and transport mode for input materials and output products ② Estimation of input materials and output products volumes based on area of each industries and basic unit of area-output ③ Estimation of input materials and output products by origin and destination, access points ④ Setting of peak hour ratio for cargo traffic and estimation of peak hour cargo traffic by access points ⑤ Selection of traffic parameters (average load by vehicle type, pcu convert factors, etc.） ⑥ Estimation of peak hour pcu traffic volume by traffic mode and access points Source: JICA Study Team Figure 7.6: Work Flow for Freight Traffic Demand Estimate Final Report - Krishnapatnam Industrial Node Development Plan PwC/Nippon Koei 160 Identification of input materials and output products by industry, Assumption of origins and destinations, packing type, and transport mode for input materials and output products Kinds of Input materials and output products are identified for each industry in node.
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