URBAN TRANSPORT AND LAND USE PLANNING ... - CiteSeerX

0 downloads 0 Views 574KB Size Report
Jul 24, 1994 - Metropolitan Area (BMA) in West Java, Indonesia, selected as a case study. ... but also in the distance as well as the sprawling growth of urban area. .... follow: kk = 0.400 (all links assumed to be in the normal gradient less ...
1702

URBAN TRANSPORT AND LAND USE PLANNING TOWARD THE SUSTAINABLE DEVELOPMENT (CASE STUDY OF BANDUNG METROPOLITAN AREA) Harun Al-Rasyid S LUBIS Senior Researcher Transport Research and Development Group, R & D Center for Infrastructure and the Region, Institute of Technology, Bandung Jl. Ganesha No. 10, Bandung – 40132, Indonesia, Telp./Fax.: +62-22-250-2350 e-mail: [email protected]

Muhamad ISNAENI Research Associate Transport Research and Development Group, R & D Center for Infrastructure and the Region, Institute of Technology, Bandung Jl. Ganesha No. 10, Bandung – 40132, Indonesia, Telp./Fax.: +62-22-250-2350 e-mail: [email protected]

Subagus Dwi NURJAYA Senior Researcher Center for Road Research and Development Ministry of Regional Settlement and Infrastructure, Indonesia Jl. Raya Timur 264 Ujung Berung Bandung – 40294, Indonesia Telp./Fax.: +62-22-250-2350 Abstract: Emphasize to the issues of environment preservation and economic equity become more important in setting the strategy for the future of urban land use and transport system. Hence, for the future of sustainable urban transport planning, the policy objectives should be underlying both of the economic and the environment issues, such as minimizing the transportation cost, emission reduction, and promoting the renewable energy. This paper examines the effectiveness of some urban transport measures in achieving those sustainability objectives. The measures include land use or zoning control, toll road development, light rail transit (LRT) operation, traffic management and vehicle technology alternative. Bandung Metropolitan Area (BMA) in West Java, Indonesia, selected as a case study. Present and future traffic indicators were estimated using standard traffic assignment technique, while emission and energy consumption was then estimated based on link characteristics. Each urban transport measure to some degree performs better than the business as usual case. The LRT alternative performs effectively; as it has the lowest travel time, emission as well as energy consumption, then followed by land use policy and vehicle technology alternatives. Keywords: sustainable urban transport, land use planning, traffic indicators, economic indicators, environment indicators. 1. INTRODUCTION Urban population growth does not only mean that there will be more people that living and working in, but also mean that there will be more loading of passenger and freight travel in the urban transport network. Increase in travel does not only prevail in the quantity of travel,

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

1703

but also in the distance as well as the sprawling growth of urban area. The selected strategy in dealing with urban transport problems will determine the level of impact experienced by the people and their environment. It is widely accepted that well managed urban transport system deeply influences the overall urban economic and prosperity of the community, whereas inefficient urban transportation will become the prime-suspect of the decreasing of livability, competitiveness, and sustainability of a city. Sayeg et al. (1992) reported that in Bangkok congestion and air pollution have reached a serious level even there were only 72 motor-vehicles/1000 peoples. Midgley (1994) said that in peak-hour the road traffic in Jakarta only moved less than 10 km/h even there were only 142 motor-vehicles/1000 peoples. Barter et al (1994) predict that fast growth cities such as Surabaya and Manila will face similar problem since no sufficient transport policy that could mitigate congestion and air-pollution problem. LPM-ITB (1992) also reported that in Jakarta, Bandung, Surabaya, and Medan, transport sector contributes more than 55.2% to the total air pollution in those four metropolitan cities. The problems turned on by the in-sufficiency of transport capacity; this could be positioned by the lack of transport supply, mismanagement of the utilization of transport facility, sprawling of land use and travel demand pattern, in efficient of vehicle capacity, etc. Urban area in development countries, as Indonesia, specified by unique problems in the urban transport operation, such as non-transport activity on the roadway, un-managed of on-street parking, un-structured of public transport network, and un-controlled of urban development. In fact, as unemployment rate increases in line with financial crisis in Indonesia, the road network capacity contracted because of roadside vendor business penetration to the sidewalk and the roadway, uncoordinated on-street parking, all of which may reduce roadway capacity by 50%. Policies to resolve urban transportation problems in Indonesia seem to follow the predict-andprovide mood with road building spirit in the mainstream agenda. This policy and planning approach has been widely proven fail to solve those problems comprehensively. New road building only can provide temporary solution; because in under-supplied of transport capacity circumstance; the compressed demand will immediately fulfilled this addition of capacity. The most probable policy options that had been widely proven more effective to solve the transport problems and the environment decease in the urban area is the Transport Demand Management (TDM). TDM strategies for sustainable development based on three basic programs; first, optimizing the existing of transport supply, second, managing the demand, and third, use environmental friendly transport mode. Optimization could be conduct by: - Maximize the utilization of road for traffic (e.g. on-street parking restriction, remove other sources of side friction), - Reduce the number of car in the road network (e.g. traffic restraint, promoting public transport, car sharing), - Manage the travel demand (e.g. land use restructuring, smart travel behavior), - Use enviromental friendly transport mode (e.g. vehicle combustion enhancement, alternantive fuel, renewable energy) This paper shows that a combination of some mitigated TDM policies is the most probable solution for sustainable transportation that could mitigate the urban traffic congestion and its impact to the economy, human, and environment.

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

1704

2. APPROACH AND THE METHODOLOGY The modeling approach in this research based on the four stages transport model (trip-based approach) to obtain traffic indicators. Link-based approach using classified traffic count data applied to obtain VMT and VHT by vehicle type. Then by adding the generalized cost model, fuel consumption model, and emission factors could be estimate the economic and environment indicators. Figure 1 shows the general structure of the modeling approach. Trip assignment conducted using SATURN (Van Vliet, 1994). Required input data adjusted to meet the characteristic of road and traffic condition in Indonesian urban area, especially for the time/cost-flow relationship. SATURN’s assignment model only simulate the alternateuser of road network (private and freight car), where road-based public transports treated separately and assumed as a fixed-flow on the each road link in their route. TRIP BASED FOUR STAGES TRANSPORT MODEL

Land use and economy Ex: land use planning

Trip generation model Trip Production

Public transport operation Ex: LRT introduction

Trip distribution model Travel pattern (origin/destination)

Road network supply Ex: new road

Policy alternatives

Modal split model Mode choice

Road transport operation Ex: parking policy

Trip assignment model

Vehicle technology Alternative Ex: alternative fuel

Aggregated traffic indicators (flow, time, speed)

Classified Traffic Count Data Vehicle Hours of Travel (VHT)

Value of Time (VOT)

Generalized cost model

Time resource consumption Total system cost

Vehicle Miles of Travel (VMT)

Fuel Consumption Model Fuel Consumption (By fuel types) Emission Factors Emission production (by gas types)

LINK-BASED-ANALYSIS

Figure 1. General Structure of Modeling Approach

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

1705

2.1 The Four Stages Transport Model 2.1.1 Appropriate Model for Each Stages Trip-based four stages transport model employed to deliver the representation of each policy alternatives in the model and, at the end, to obtain traffic indicators. Considering the availability of existing data and calibrated model, especially for Bandung Metropolitan Area (BMA) case, in Table 1 presented the most appropriate trip-based model used in this research. Table 1. Appropriate Four Stages Transport Model for BMA Case Stage Trip generation Trip distribution

Model Trip Rate Model (1) Matrix Estimation from Maximum Entropy (ME2) (2) Furness Model

Source/Author BMARTS Study (1995) (1) Willumsen (1982)

Modal split

Diversion curve

BMARTS Study (1995)

Trip assignment

Wardrop-Equilibrium

Frank and Wolfe (1956)

(2) Furness (1965)

Note Trip rate based on zone aggregation (1) ME2 model in SATURN used to calibrate the base-year 1997 OD matrix (2) Furness model used to estimate the future OD matrices 2002-2017 The curve shaped by binary logit model represents competition between private and public transport mode SATURN used to conduct traffic assignment simulation

2.1.2 Link Speed-Flow Relationship Models to allocate the trip to each origin-destination (in trip distribution stage) and/or to each link in the road network (in trip assignment stage) usually based on the time and distance comparison among alternatives. Estimation of travel distance and travel time in road network derive from speed-flow relationship curve on each road link. SATURN using a universal speed-flow relationship as follows: t = a Vn + to for V < C t = a Cn + to + b (V – C)/C for V >C

(1) (2)

Where C (pcu/hour) is the link capacity; to (sec.) and t (sec.) respectively represents the travel time at free-flow condition and travel time at traffic volume on this link is V (pcu/hour); and a, b, and n are constants. Logarithmic-manipulation applied on the Indonesian road link speed flow relationship (IHCM, 1997) using several combinations of link data of distance, capacity, and volume; the process resulting the value of n in around of 1.23, and varying a, b values depends on the link characteristics. 2.2 VMT (Vehicle Miles of Travel) and VHT (Vehicle Hours of Travel) Estimation Once the trip assignment (using SATURN) conducted, then we can obtain traffic information on each link in the network. For example: link a with la km length, loaded by traffic in amount of va pcu/hour and running in Va km/hour; then total travel length on link a is va * la pcu.km/hour, and the total travel time is va *(la/Va) pcu.hour/hour. If the value of passenger car equivalent (pce) and link vehicle composition data (v’akn = flow of k type of vehicle on link a,

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

1706

where link a included in the k road category) were inserted to the total travel length and total travel time in link a then we could obtain the VMT (vehicle.km/hour) and VHT (vehicle.hour/hour). Figure 2 shows the Methodology to estimate VMT and VHT.

VMT and VHT Estimation

Survey Data

SATURN Output

In order to meet the operational characteristic of each mode, the link flows were disaggregated in 7 categories, i.e. gasoline-car, diesel-car, motorcycle, public transport minibus, taxi, bus, and truck. The pce using the IHCM 1997 value as follow: 1 for light vehicle (car, jeep, minibus, taxi), 1.2 for heavy vehicle (truck and bus), and 0.25 for motorcycle. Vehicle composition obtained from classified traffic count data that instantly classified in to 9 road categories as the combination of functional classification (arterial, collector, and local road) and the locational classification (city center road, middle-area road, and urbanized-area road). Traffic flow at link a = va pcu/hour

Average speed at link a = Va km/hour

Total travel length on link a = va *λa pcu.km/hour

Total travel time on link a = va *(λa/Va) pcu.hour/hour

Classified traffic count on link a = v’a = ∑n v’akn pcu/hour 1

Total travel length by vehicle types on link a VMTa = ∑ n v’akn *λa veh.km/hour 1

Sistem’s total travel length by vehicle types VMT = ∑ a ∑ n v’akn *λa veh.km/hour 1

1

Passenger car equivalent (pce)

Total travel times by vehicle types on link a VHTa = ∑ n v’akn *(λa/Va) veh.hour/hour 1

Sistem’s total travel times by vehicle types VHT = ∑a ∑n v’akn *(λa/Va) veh.hour/hour 1

1

Figure 2. Estimation process to Obtain VMT and VHT 2.3 Environment Indicators Estimation The environment indicators (total system fuel consumption and emission production) estimated using the methodology as explained in Figure 3. Having done the assignment stage we have link a average speed (Va) and the Ratio of Volume/Capacity (V/Ca); then we could estimate the specific fuel consumption of vehicle type k on link a (FCSak) using the PT. Jasa Marga and LAPI-ITB (1996) equation as follow: FCSak = bf (1 ± (kk + kl + kr))

(3)

Where the bf is the value of basic fuel consumption in litre/1,000 km, kk is correction factor for slope, kl correction factor for traffic condition, and kr is correction factor for roughness.

Journal of the Eastern Asia Society for Transportation Studies, Vol.5, October, 2003

1707

Link a average speed (Va) and Volume/Capacity (V/Ca)

Specific fuel consumption of vehicle type k on link a = FCSak = bf(1±(kk+kl+kr))

VMT for vehicle type k on the link a = VMTak

Fuel consumption of vehicle type k on link a FCak = FCSak * VMTak

Fuel consumption on link a FCa = ∑1k (FCSak * VMTak)

Emission factors of vehicle type k = FEk

Emission of vehicle type k on link a Eak = FCak FEk

System’s fuel consumption FC = ∑1a (FCa )

Emission on link a Ea =∑1k ( FCak* FEk)

System’s emission FE =∑1a (Ea)

Figure 3. Environment Indicators Estimation Process Basic fuel consumption for each vehicle category given as follows: bf (category I) = 0,0284 V2 - 3,0644 V + 141,68 bf (category II A) = 2.26533 * bf (category I) bf (category II B) = 2.90805 * bf (category I)

(7) (8) (9)

Vehicle Category I including passenger cars (sedan, jeep, pick up, and minibus), ¾ truck, and medium bus. Vehicle Category II A including (big) truck and bus with 2 axles, and Vehicle Category II B including (big) truck and bus with 3 axles or more. Correction factors of kk, kl, and kr using the value of PT. Jasa Marga and LAPI-ITB (1996) as follow: kk = 0.400 (all links assumed to be in the normal gradient less than 5%), kr = 0.035 for arterial roads and kr = 0.085 for others, and kl will depends on the V/C on each link where: kl = 0.050 (for V/C less than 0.6), kl = 0.185 (0.6 < V/C