Sensitivity Analysis of Passenger Volume for Public Bus Services: Case Study of Penang Island, Malaysia

Transcription

1 Sensitivity Analysis of Passenger Volume for Public Bus Services: Case Study of Penang Island, Malaysia Lee Vien LEONG Lecturer School of Civil Engineering, University Sains Malaysia, Engineering Campus, Nibong Tebal, Pulau Pinang, Malaysia Tel: Nurikhwani Idayu ZAINAL ABIDIN PhD student School of Civil Engineering, University Sains Malaysia, Engineering Campus, Nibong Tebal, Pulau Pinang, Malaysia Tel: Younes BAGHERI MSc student School of Civil Engineering, University Sains Malaysia, Engineering Campus, Nibong Tebal, Pulau Pinang, Malaysia Tel: Ahmad Farhan MOHD. SADULLAH Professor Deputy General, Malaysian Institute of Road Safety Research (MIROS), Lot , Jalan TKS 1, Taman Kajang Sentral, Kajang, Selangor Darul Ehsan, Malaysia. Tel: Abstract: In order to encourage more people to use public transportation, a better public transport system should be provided and improved from time to time. Numerous softwares have been developed to aid in the planning of transportation system. One of the software which is known as EMME/2 was used in this study. The EMME/2 was used to calculate the boarding volume of buses. Based on a case study, sensitivity analyses were conducted to determine the most influential parameters affecting the boarding volume. From the study, it was concluded that the most significant parameter for this case study is the wait-time weight, followed by the boarding-time weight while other parameters do not affect the boarding volume much. Key Words: Public transport, passenger volume, bus services 1. INTRODUCTION Public transport has always been an alternative for the environmentally-conscious road users. In Malaysia, particularly in Penang, due to limited choices, the public bus is the most preferred alternative. However, most road users still favor the comfort of private transport with the most common reason of this choice being that public transport is unreliable. This may be due to the personal experience of the road users or their opinion by observation. Either way, this shows that the prospect of using public transport is still deemed unattractive therefore it is not the main choice for road users. In order to encourage more road users to use the public bus service, an improved public transport system should be developed particularly for the public bus. This system should then be updated from time to time. This is to ensure that the public bus system is always in its optimum condition that will, as an end result, attract more and more road users to switch to public transport. 1193

2 However, it is not easy to do. When planning for a better public transport system, changes are unavoidable and for changes to happen, the sources of the inefficiency of the current system must be identified, therefore efficiency analysis would come as an advantage (Avkiran 2009). The reason for the efficiency analysis is to identify what attributes would influence the users choice in public transport (D'Acierno, Cartenì et al. 2009). In order to develop a better public bus system, the system itself needs to be understood. More number of buses does not actually mean that it would result in a better system. In fact, sometime a lower number of buses can result in a better system if certain attributes, such as bus scheduling, is improved (Fügenschuh 2009). For the public bus system, the efficiency of the level of service of the system is usually measured the time spent waiting for the bus as well as the boarding time for the whole journey (Pacheco, Alvarez et al. 2009). This is understandable as complaints of the inefficiency of the public transport system are almost always associated with time wasted unproductively. Spiess and Florian (1989) formulated the bus transit assignment problem in a linear optimization framework. The optimization problem was called Optimal Strategy and a 2-step solving algorithm was proposed for that. The algorithm finds the optimal strategy at the first step and then assigns the demand to that strategy. Babazadeh and Aashtiani (2005) formulated the transit assignment problem in a series of complementary equations and replicated the congestion effect in the bus transit network perfectly. Due to the size and also nonlinearity of the complementary model, it was almost impossible to find the equilibrium solution for an extensive network. With the growing interest in the field of transportation planning, a lot of softwares have been developed to aid as analysis and planning tools. This software have provided traffic engineers and transportation planners with powerful and flexible tools in modelling traffic network for analysis and planning as they can be used to predict the travel demand and are also able to design optimum transportation networks. This software is also able to carry out traffic impact studies. One of this software is the EMME/2 which is used for this study. 2. METHODOLOGY Bus services provided in the sate of Penang, Malaysia is the case study in this paper. Penang State consists of two parts the small island of Penang and the larger coastal territory of Seberang Perai on the peninsular mainland. The two geographical areas are linked by bridge and a ferry system. Penang Island is the second smallest of the thirteen states of Malaysia, with an area of only 1,031 square kilometers and geographically of Penang Island is surrounded within N 5 28 to N 5 15 and E to E from north to south and N 5 25 to N 5 22 and E to E from east to west. Figure 1 illustrates the location of Penang Island in Malaysia. 1194

3 PENANG ISLAND Figure 1 Location of Penang Island in Malaysia Penang island is an important island in Malaysia because primarily, it is the second largest city in Malaysia after the federal capital Kuala Lumpur and secondly, contains diverse ecosystems including hill forests, coastal forests, sandy coastlines that have a fine potential for trip attraction. A high increasing number of cars and motorcycles ownership in the state of Penang has resulted in traffic congestion, with increased delays and travel times, higher rates of injury and death from traffic accidents which lead to the necessity to have a more efficient public transportation system. The public transportation system in Penang Island consists of taxis, buses and trishaws. Most of the city taxis do not used the meter system but the passenger may insist on the meter being turn on or agree on a price before taking off. As for buses, most of the buses use coin machines to collect fares. The main bus terminals are at Pengkalan Weld which is also the ferry terminal and at the ground floor of Tun Abdul Razak Complex or better known as KOMTAR as well as the newly opened Sungai Nibong Express Bus Terminal. Apart from taxis and buses, trishaw services are also provided within the city area where they are mainly used for sightseeing purposes. Even though improvements have being made to the bus system in the past few years such as the introduction of minibuses and expansion of bus routes, the popularity of public transport among residents in Penang still remains very low. Therefore, with the realization that traffic 1195

4 problems in Penang state have reached a critical level, the state government has decided to revamp and restructure the existing bus services and subsequently, a new bus route zoning system which covers a wider area was implemented as of 1 st April Under the new system, stage buses were only allowed to operate at major roads in the city while mini buses will complement the stage bus services by covering social routes in the outskirts. However, based on the various reports published in the newspaper, the new bus system still faces many problems and the performance of the bus services in Penang has yet to improve. One of the issues raised is the inefficiency of bus companies and present operators who failed to conform to rules when issuing tickets and keeping the buses clean and in good order. The main reason given by bus operator on the poor bus services is that operators are not able to sustain their service, as costs have gone up. Therefore, operators often blame this situation for the poor service they offer, as they cannot afford to improve the bus condition and the general level of service. Hence, transit system in Penang has further revamped as the State Government has given up hope on existing operators ever improving their services. With this regard, in July 2007, a company known as Rapid Penang Sdn. Bhd. has started operation with a fleet of 150 buses. The objective of this transit system was to provide a comfortable, affordable and reliable public transport services for the residents in Penang. Out of 150 buses, 110 buses are deployed on the island with 24 transit lines while the other 40 buses with 9 transit lines are deployed on the mainland. However, due to the reason that this is a case study of Penang Island, therefore this paper only focused on the transit network provided by Rapid Penang Sdn. Bhd. in the island. Figure 2 shows the transit network operated by Rapid Penang Sdn. Bhd. in Penang Island. Currently, Rapid Penang Sdn. Bhd. is the main bus operator in Penang. Figure 2 Bus road network for Penang Island 1196

5 In transit modeling, input parameters are very important. Therefore, in order to determine the important parameters that greatly influence the outcome, a sensitivity analysis is carried out. A sensitivity analysis is an instrument for the assessment of the input parameters with respect to their impact on the model output. It is useful not only for model development, but also for model validation and reduction of uncertainty. In this research, the Nix method (Nix, 1994) was applied for sensitivity analysis of EMME/2. The transit-assignment procedure, implemented in EMME/2, is used to determine the passenger volumes on each transit route by assigning the origin destination (O D) transit matrix to a computerized transit network, which consists of nodes, transit links, and walk links. A node is the point at which several transit links or walk links connect with each other. There are two important attributes for each transit line which are headway and segment time. Both attributes may affect the transit-rider s decision on which route to take to reach their destinations. Walk links connect trip origins to the nodes at which travelers can board and alight a transit vehicle. The aggregate transit-assignment model is based on a rigorous theoretical foundation, which assigns transit trips based on optimal strategies. The optimal-strategy concept defines a set of paths from origin to destination that minimizes the total expected travel time. To run the transit-assignment module in EMME/2, the user needs to specify the values of five parameters. They are boarding time, wait-time factor, wait-time weight, boarding time weight, and auxiliary time weight. These are the attributes that are applicable to used in the sensitivity analysis. Boarding time is the time required for every boarding, which reflects the penalty added to the O D trip impedance. It can be applied as the same value for the whole network or can be applied as a node/line-specific value. Wait-time factor is a parameter used for capturing the passenger arrival distribution and its effect on waiting times at transit stops. Wait time weight, boarding time weight, and auxiliary time weight are used to quantify the perception of waiting time, auxiliary transit time and boarding time, with respect to the vehicle time. These weights must be between 0.00 and For example, a weight of 2.5 for the waiting time means that travelers perceive 1 minute spent waiting as equivalent to 2.5 minutes spent on-board a vehicle (Emme/2 User s Manual, 1999). For the sensitivity analysis, each parameter is tested individually. For each chosen parameter, changes of ±2%, ±5%, ±10%, ±20%, ±30%, ±50% and ±75% is applied and simulated while the other parameters remain constant. The affect of these changes are then observed and discussed. 3. DATA COLLECTION Transportation modelling software EMME/2 was used to build the transit network in Penang Island with a fixed demand matrix. A detailed zoning system (35 zones covering an area for island) was developed. This transit network in EMME/2 includes four transit vehicle types, 973 regular nodes, 3170 transit line segments and 2090 directional links. A demand matrix (OD matrix) was built for the peak period based on a survey of 1700 passengers which was conducted from September 2007 to February Data such as headway, layover time, length, travel time, speed and passenger volume were collected during morning, afternoon and evening peak periods and averaged for each transit line as shown in Table 1. In order to 1197

6 obtain the results of sensitivity analyses which can clearly represent the real situation, observed headways were used instead of the scheduled headways. Nevertheless, based on the results obtained, headways collected during the three peak periods are almost the same, therefore the assumption of uniform bus headways is still true in this case. Figure 3 shows the itinerary of the transit lines. Transit Line Headway (minutes) Table 1 Basic Information of transit lines Layover Total Total Travel Time Length Time (minutes) (km) (minutes) Transit Speed (km/hr) Passenger Volume U U U U U U U U U U U U U U Figure 3 Itinerary of transit lines in Penang Island 1198

7 4. RESULTS AND DISCUSSION Results for the sensitivity analysis of boarding time are shown in Table 2 and Figure 4. Based on Table 2, significant percentages of changes were observed throughout the analysis. From the results denoted in Table 2, it appears that the outputs of the modelling (total passenger volume) were sensitive to the boarding time parameter. Boarding time has inverse relation with total passenger volume. For the most sensitive transit line which is U307, with the decreased of the boarding time to 75%, the total passenger volume increased by 24%. On the contrary, if the initial value of boarding time is increased to 75%, the passenger volume decreased to 8.5% in the transit line U307. Table 2 Result of sensitivity analysis for boarding time Change in Initial % Change in Total Passenger Volume Value of Boarding Time (%) U101 U102 U103 U104 U201 U202 U203 U204 U206 U301 U302 U303 U307 U Figure 4 Sensitivity analyses for boarding time 1199

8 Similar results were obtained for the boarding time weight where almost identical results were obtained as can be seen in Table 3 and Figure 5. Similar with the case of boarding time, the most sensitive transit line for boarding time weight is also U307. Table 3 Result of sensitivity analysis for boarding time weight Change in Initial % Change in Total Boarding Volume Value of Boarding Time U101 U102 U103 U104 U201 U202 U203 U204 U206 U301 U302 U303 U307 U401 weight (%) Figure 5 Sensitivity analyses for boarding time weight In addition, wait time factor has a significant effect on the total passenger volume as shown in Table 4 and Figure 6. For the wait time factor, a higher percentage of difference in the total passenger volume was observed when the wait time factor was decreased by 75%. The maximum difference in the total passenger volume obtained was -45% in transit line U

9 However, when the wait time factor was increased by 75%, only a change of -5% was observed in the total passenger volume in the transit line U104. Apart from transit line U104, transit lines U201 and U204 are also sensitive to the wait time factor. Table 5 and Figure 7 display similar results for sensitivity analysis on wait time weight with regards to wait time factor. Also, similar with the case of wait time factor, the most sensitive transit lines for wait time weight are also U104, U201 and U204. The wait time weight parameter is used to quantify the perception of waiting time with respect to the vehicle time. Table 4 Result of sensitivity analysis for wait time factor Change in Initial % Change in Total Boarding Volume Value of wait time factor (%) U101 U102 U103 U104 U201 U202 U203 U204 U206 U301 U302 U303 U307 U Figure 6 Sensitivity analysis of wait time factor 1201

10 Table 5 Result of sensitivity analysis for wait time weight Change in Initial Value % Change in Total Boarding Volume of Wait Time Weight (%) U101 U102 U103 U104 U201 U202 U203 U204 U206 U301 U302 U303 U307 U Figure 7 Sensitivity analysis of wait time weight The final parameter tested for the sensitivity analysis is the auxiliary time weight and the result for this parameter is as can be seen in Table 6. From the table, it can be concluded that there is no change in the total boarding volume if the auxiliary time weight is changed. Therefore, is can be said that this parameter is not sensitive at all for the total boarding volume. 1202

11 Table 6 Result of sensitivity analysis for auxiliary time Change in Initial Value % Change in Total Boarding Volume of Auxiliary Time Weight (%) U101 U102 U103 U104 U201 U202 U203 U204 U206 U301 U302 U303 U307 U Overall based on the results obtained in Table 2 to Table 5, the most sensitive transit line for boarding time and boarding time weight is U307 and for wait time factor and wait time weight, the most sensitive transit lines are U104, U201 and U204. Figure 8 show the results of the sensitivity analysis with the most sensitive transit lines for all studied parameters. As for the auxiliary time weight, it is the least sensitive parameter towards total passenger volume. Figure 8 Final results for the sensitivity analysis with the most sensitive transit line 1203

12 5. CONCLUSION Based on the results obtained from this case study, it can be concluded that for total passenger volume, the most influential factors that affect the outcome would be the wait-time factor and wait-time weight. These factors are followed by the boarding-time factor and boarding-time weight which, though not as influential as the wait-time factor and wait-time weight, still significantly affects the total boarding volume. The final factor which is the auxiliary-time weight does not affect the total boarding time much. This means, during transportation modeling for Penang Island, the four former factors are the ones that should be concentrated on more. REFERENCES Avkiran, N. K. (2009), Opening the black box of efficiency analysis: An illustration with UAE banks, Omega 37(4): D'Acierno, L., A. Cartenì, et al. (2009), Estimation of urban traffic conditions using an Automatic Vehicle Location (AVL) System, European Journal of Operational Research 196(2): Fügenschuh, A. (2009), Solving a school bus scheduling problem with integer programming, European Journal of Operational Research 193(3): Pacheco, J., A. Alvarez, et al. (2009), A tabu search approach to an urban transport problem in northern Spain, Computers & Operations Research 36(3): INRO Consultants (1999), Emme/2 User s Manual: Release 9.2., Montréal. Nix, S. J. (1994), Urban Modeling and Simulation, Boca Raton, FL: Lewis Publishers. Spiess, H. and M. Florian, (1989), Optimal strategies: A new assignment model for transit networks, Transport Res. B-Meth., 23: Babazadeh, A. and H. Z. Aashtiani, (2005) Algorithm for equilibrium transit assignment problem. Transport, Res. Rec., No. 1923, Transportation Research Board of the National Academies, Washington, D.C., pp: