Modelling on the Readiness in Implementation of Bike Sharing System at University
Mohd Azizul Ladin1*, Lee Fui Jun1, Azwa Safiqah Darawati1, Muhamad Razuhanafi Mat Yazid2, Almando Abbil3, Hussin A. M Yahia4
1 Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
2 Fakulti Kejuruteraan Alam dan Bina, Universiti Kebangsaan Malaysia, Selangor, Malaysia
3 Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
4 Civil Engineering Department, Middle East College Knowledge Oasis, Al Rusayl, Sultanate of Oman
*Corresponding Author: [email protected] Accepted: 15 February 2022 | Published: 1 March 2022
DOI:https://doi.org/10.55057/ijares.2022.4.1.7
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Abstract: Private vehicle ownership in University Malaysia Sabah (UMS) has increased year after year, resulting in severe traffic congestion, particularly during peak hours. In this research, it is hypothesised that the mode of transportation chosen by UMS students may be determined by the affordability of the bike sharing system's rental fee. This research has been carried out to model on Faculty of Science and Natural Resources student’s readiness to implement bike sharing system at University Malaysia Sabah. The key objective of this research is to develop transport model of the user’s readiness to shift from the private vehicle to bike sharing system in terms of rental charging factor (per use and per hour) in Ringgit Malaysia (RM). In the conclusion of the study, a transportation model on rental charges will be established, and it is anticipated that once rental pricing is introduced, the percentage of private vehicles that are converted to bike sharing will increase. Transferring private car users to a bike sharing scheme has the potential to lower the number of private automobiles in the UMS and so assist alleviate traffic congestion. Hypothetically, this concept is seen necessary for mitigating the negative influence of private vehicles in UMS. The Stated Preference Survey was employed in this study. Questionnaires were prepared and delivered online to 160 responders. Following that, the data is analysed using linear regression to create a logistic model. The model predicts that the number of individuals ready to use bike sharing system increase linearly when the rental charging fee decreases. Therefore, as the rental charging fee reduce, more people will shift to bike sharing system.
Keywords: Parking Charging System, State Preference Survey, Linear Regression
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1. Introduction
A well-planned transportation system will help improve the efficiency of the transportation system and provide various advantages to a society (Idris et al., 2019). Kota Kinabalu is one of the biggest cities in Sabah, and it suffers from traffic congestion as a result of the large number of private vehicles on the road and the delayed construction of traffic infrastructure (Abdullah
& Hua, 2017). Same goes to the situation at University Malaysia Sabah (UMS), the number of students who choose to use private vehicle in UMS compound had increased year by year. This is because owning a car or motorcycle is now easier and more affordable than ever before, thanks to low interest rates, a simplified loan approval process, and substantially subsidised
gasoline (Abdelfatah et al., 2015). Therefore, a modelling of implementing bike sharing system in ums to solve these problems and questionnaire is distributed to test the readiness of Faculty of Science and Natural Resource (FSSA) students towards this implementation. Bike sharing is the distribution of a pool of bicycles by way of an interconnected network of strategically placed 'bike-sharing stations,' which are typically located throughout an urban area and accessible to a diverse range of different types of users (Ricci, 2015). Due to a lack of natural resources, such as oil, most cities' transportation systems are no longer sustainable, leading to a surge in traffic fatalities and damages (Yazid et al., 2011). Both public transport and private vehicles can cause environmental problems which may lead to traffic congestion, pollution and high consumption of non-renewable energy. In Malaysia, the transportation industry accounts for 28% of total CO2 emissions, with 85% coming from road transport (Mustapa & Bekhet, 2016). One of the mitigation strategies to minimize the CO2 emissions is by constraining the fuel cost. However, this strategy does not solve the problem thoroughly. Therefore, this paper attempts to purpose a sustainable transportation system which is bike sharing system.
One of the objectives of this research is to identify the elements from the implementation of bike sharing system that encouraging and obstructing UMS students to reduce the use of private vehicles. The data collection to the readiness of UMS students to shift from private vehicle to non-motorized bike sharing system will be analysed. Lastly, a modelling on FSSA’s student readiness on implementation of bike sharing system at UMS is developed and a hypothesis of
“the percentage students choosing bike sharing system as transportation mode will increase as the bike sharing system price decrease” may be exploited.
2. Literature Review
The Majority of the people are using private vehicles for their daily life activities due to the high comfortableness compare with other transport methods. However, there are also few disadvantages that brought by excessive number of private vehicles causing big impacts to the world. The transportation sector is also the primary source of air pollution, creating a massive demand for non-renewable resources such as fossil fuels (Yazid et al., 2011). The examples for these disadvantages are causing in the present circumstances, majority of people choose private vehicles as their main transportation for daily life activities. Private vehicles indeed bring a lot of benefits to people as it is more convenient to travel instead of always follow the fix routes and timetable and have more personal space as compared to other transport method. However, excessive number of private vehicles will also bring bad impact to the traffic condition. Rapid economic growth and increased urbanisation have resulted in a significant increase in urban traffic demand, which has outpaced the capacity of urban road networks to maintain and manage them, exacerbating urban traffic congestion (Shi et al., 2019). As the number of students who bring their own cars to University Malaysia Sabah (UMS) grows, so does the traffic congestion problem. During peak hours, which are 7.00am to 8.00am in the morning, noon 12.00pm to 2.00pm, and 4.30pm to 6.00pm in the evening, traffic is significantly worse.
Students are allowed to drive their own vehicles on campus as long as they comply with University Malaysia Sabah (UMS) safety regulations and register them properly. Car parking spaces are in jeopardy because of the large number of vehicles concentrated in a single city Even though many solutions have been implemented, such as parking charging system the problem of a shortage of parking spaces has yet to be solved (Besar et al., 2020). Thus, transportation systems in most cities are no longer sustainable and can cause environmental problems which may lead to traffic congestion, pollution and high consumption of non- renewable energy. However, a well-planned public transportation can contribute to the reduction of problems associated with a variety of transportation externalities, including
accidents and traffic congestion. As such the sustainable transportation results in a well- balanced transportation system that makes optimal use of all modes of transportation (Ladin et al., 2014). Thus, a well-managed transportation system will undoubtedly provide numerous benefits to a society and country by promoting sustainable transportation (Idris et al., 2019).
Approximately 500 bike-sharing schemes operate globally at the time, with almost 500,000 bikes shared. Today, these systems generate substantial interest due to their vital role in transportation, the environment, and public health. (Tekouabou, 2021). As indicated by the recent growth in the number of bicycles in major cities throughout the world, bike-sharing networks have become an important component of urban mobility policy over the last decade.
The term "Multi-modal Systems" refers to bike sharing system that integrates GPS data of bicycle ridership, actual stats on ridership, mobile bike stations, and system integration with public transportation modes (Shaheen et al., 2013). Most public bicycle sharing programs serve both loyal customers who pay annual subscription fees and casual users who pay on a per-ride or month-to-month basis, depending on the program. Users are permitted to ride the bicycle for a maximum of 30 minutes before returning it to the next docking station. There are several different types of bike sharing systems that have been implemented in various cities, including:
• Mobike
• Lime Bike
• Citi Bike
• Jump Bike
• O Bike
There were many studies have been conducted on the topic of logistic model and transportation.
A study on bike-sharing initiatives has collected information on the effects of bike-sharing on traffic, the environment, and public health. It has also accumulated information on the social impacts of bike-sharing, the management of bike-sharing programmes, and the development of bike-sharing programmes that are sustainable (Qiu & He, 2018). Expediency was the top factor in users' desire to join, followed by the closeness of bike sharing stations to their workplace (which is also connected with accessibility), whereas car comfort was one of the primary hurdles to join for non-users (Teixeira et al., 2021). According to Texeira et al. (2021), another factor that influences bike sharing adoption is the system's ease of use. Features such as the amount of time and effort required to register and use the system, as well as the hours of operation, can all have an impact on the decision to participate or not participate in bike sharing.
Concerns have been raised about the impact of automated bicycle rentals on bicycle usage due to the rapid growth of the industry. Despite the fact that they are beneficial for cities, they can also be extremely difficult to manage when adverse weather conditions prevail (Gebhart &
Noland, 2013). Another limitation was the inclusion of a small number of health outcomes associated with air pollution and physical activity, despite the fact that these exposures have been linked to the most health outcomes. In addition to that, research analysis focused exclusively on the long-term consequences of bike sharing system trips, leaving out any potential short-term risks or benefits associated with air pollution exposure or physical activity (Clockston & Rojas-Rueda, 2021).
According to Cherchi & Hensher (2015), a state preference survey is manipulated in order to elicit behavioural responses and to allow identification of an individual or a group of individuals through estimation of their preferences. SP surveys have evolved as the primary data paradigm for investigating individuals' behavioural market decisions in the marketplace due to its capacity to facilitate the identification of individual preferences in a framework that
efficiently decreases the respondent's cognitive effort and weariness. The stated preference survey is adopted throughout this research in order to assess FSSA students’ preferences regarding bike sharing system introduction while analysing the variables that affecting them in choosing this mode and the range of price for bicycle renting in Universiti Malaysia Sabah (UMS). In linear regression model, two kinds of variables are used. The first type is dependent variable, which is a variable that depends on other variables and the second type is independent variable, which is a variable that is not bound by other variables (Permai & Tanty, 2018). For the purpose of modelling the linear relationship between two variables, simple linear regression is used. The dependent variable y is one of them, whereas the independent variable x is another.
The simple regression model is frequently expressed in the following format:
𝑦 = 𝛽0+ 𝛽1𝑥 + 𝜀 (1) Where,
𝑦 = the dependent variable
𝑥 = the independent variable
𝛽0 = 𝑦-intercept
𝛽1 = the gradient or the slope of the regression line
It is expected that error 𝜀 is normally distributed with 𝐸(𝜀) = 0 and a constant variance 𝑉𝑎𝑟(𝜀) = 𝜎2 in the regression. For this study, the two variable is bicycle rental charging (per use and per hour) and gender.
3. Methodology
Universiti Malaysia Sabah (UMS), Kota Kinabalu, Sabah was chosen as the study area of this study. The respondents were students that randomly chosen from Faculty of Science and Natural Resource in UMS. The readiness of road users to shift from private vehicle to bike sharing transportation will be analysed through data collection and data interpretation. The data collection technique was carried out in this study by sending online questionnaires to undergraduate and postgraduate students of FSSA at UMS. The sample size required is 325 students from various programmes and years, as determined by the relationship between sample size and total population (Januszyk et al., 2011).
A short description of this research was included in the questionnaire to give more understanding. Fully paperless method is used to fit the image of UMS Eco Campus which is one of the main ideas of sustainability on the campus. Therefore, using Google Forms as the tool to send out the questionnaires is the most appropriate way to decrease the usage of paper and achieve the idea of Eco Campus. The stated preference survey (SPS) method was used in the questionnaire to obtain the data required in this research. The data obtained were analyzed by using linear regression to develop a logistic model.
The questionnaire consists of 14 questions which comprise of demographic data, public transport evaluation, and bike sharing mode. Aggregation is a term that refers to the process of compiling data and presenting it in a summarized form. The collected data is analyzed and a bike sharing model is developed in this research using linear regression analysis. In the same time, linear regression is used to study the linear relationship between a dependent variable Y (Percentage of Private Vehicle User’s Shift to Bike Sharing Transportation) and one or more independent variables X (Rental Prices and Another Factor). The logistics functions that are used in this transport modelling are shown in equation 2:
P= 1/ (1+De^ (αx+ βy)) (2) Where,
P = Probability
α & β = Coefficient to be calibrated x & y = Independent variables D = Constant
4. Discussion and Conclusion
The number of respondents is deducted from 327 to 160 students from FSSA due to the enforcement of movement control order. Google Forms is used as a service to distribute the questionnaire forms. Hence, some students have difficulties to access due to poor internet connection especially those who stay at rural area. For this study, there are more female students had response to this survey compared to male students which are 76.3% and 23.8%
respectively. One of the possible reasons is that majority of FSSA students are female. Next, 97.5% of the respondents are from Malaysia. Most of the respondents are from third year.
Furthermore, 57% of the respondents are using public transport which is bus as a mode of transportation used in UMS and 34% of the respondents has their own car or motorcycle to move inside and out of UMS and the rest of respondents prefer to walk and use bicycle as mode of transportation. Besides, for the rating of service of public transport in UMS, it show that the quality of public transport’s services in UMS is considered good in terms of comfortability but students might still need to stand on bus during peak hours and the rating for convenient and speed indicates that students are confusing with the waiting station and timetable for bus routine.
In addition, it is found that the comfortability of private transport has the greatest influence on students to use private transport which is 70.6% out of all the respondents from UMS FSSA students. Besides, factors that discourage students the most which is 47% of them in using private transport is due to inadequate parking availability in faculty and especially during exam season. The agreeableness with the implementation of bike sharing system is carried out and 67.5% of female respondents and 20.6% of male respondents are willing to implement bike sharing system at Faculty of Science and Natural Resources. Besides, it is found that the greatest percentage that motivates the students to use bike sharing system is reduced fuel consumption and congestion with 72.5%. This is because implementation of bike sharing system able to reduce the use of private vehicle and eventually reduce fuel consumption and congestion. Meanwhile, 65% of FSSA respondents not willing to use bike sharing system due to hilly topography from residential college to faculty. It is very difficult for a student to commuting back and forth by using a bicycle where going up the hill may consume more strength.
For the bicycle rental charging method, respondents are more willing to choose charge per use rather than charge per hour as bicycle rental charging with 19.4% of male and 65% of female.
Figure 1 shows that 40.6% of the respondents are willing to shift from private transport to bike sharing system by rental price of bike sharing per use with rental price RM1.50 per use.
Simultaneously, 84.4% of the respondents are willing to shift from private transport to bike sharing system by rental price of bike sharing per hour with rental price RM1.50 per hour
Figure 1: Percentage of Students Shift to Bike Sharing
The aggregated data are required for the analysis and the appropriate data will be selected before proceeding to analysis. In this data analysis, there are several main independent variables can be analyzed namely:
i. Probability of private transport users to shift to bike sharing system based on rental charging per use
ii. Probability of private transport users to shift to bike sharing system based on rental charging per hour
iii. Probability of private transport users to shift to bike sharing system based on gender Moreover, regression analysis is conducted for data from rental charging method of both charged per use and charged per entry. Figure 2 and Figure 3 below show the comparison values between actual P and model P for willingness of private vehicle users to shift to public transport with rental charging method per use and per hour. By using the regression analysis which is completed by using Microsoft Excel, charge per use is having R2 value which is approaching to 1 while charge per hour is not. This show that charge per use data are having stronger relationship and smaller distance between the data points.
Figure 2: Comparison for the Willingness of Private Vehicle Users to Shift to Bike Sharing System
Figure 3: Comparison Values for the Willingness of Private Vehicle Users to Shift to Bike Sharing System
The result from regression analysis is shown below:
R Square, R2 = 0.085731
Standard Error = 2.148275
Intercept of Y-axis = 0.426891 The coefficient of X variable = 0.629016
The R square, R² is an indicator to evaluate the model-response relationship strength, ranging from 0 to 1. For this analysis, the value is not approaching to 1. This shows that the and 𝑦 are estimated to have a quite weak relationship. Besides, The regression's standard error indicates the average gap between the observed values and the regression line.
Therefore, the linear regression equation become as follow:
𝑦 = 0.629016𝑥 + 0.426891 (3) From the equation, it shows that:
ln D = 0.426891 D = 1.532486
𝛼 = 0.629016
The logistic model for the willingness of private vehicle users to shift to public transport can be present in equation below:
𝑃 = 1
1+.1532486𝑒0.629016𝑥 (4)
Furthermore, Figure 4 and Figure 5 show the comparison values between actual P and model P for willingness of female and male private vehicle users to shift to bike sharing system. The result shows that the x and 𝑦 for female have a stronger relationship than male.
Figure 4: Comparison Values Between Actual P and Model P for the Willingness of Male Vehicle Users to Shift to Bike Sharing System
The result from regression analysis is shown below:
R Square, R2 = 0.252971
Standard Error = 1.086533
Intercept of Y-axis = -1.23272 The coefficient of X variable = 0.604575
The R square, R² is an indicator to evaluate the model-response relationship strength, ranging from 0 to 1. For this analysis, the value is not approaching to 1. This shows that the and 𝑦 are estimated to have a quite weak relationship. Besides, The regression's standard error indicates the average gap between the observed values and the regression line.
Therefore, the linear regression equation become as follow:
𝑦 = 0.604575𝑥 − 1.23272 (5) From the equation, it shows that:
ln D = -1.23272 D = 0.291499
𝛼 = 0.604575
The logistic model for the willingness of male private vehicle users to shift to public transport can be present in equation below:
𝑃 = 1
1+0.291499𝑒0.604575𝑥 (6)
Figure 5: Comparison Values Between Actual P and Model P for the Willingness of Female Vehicle Users to Shift to Bike Sharing system
The result from regression analysis is shown below:
R Square, R2 = 0.923426
Standard Error = 0.502801
Intercept of Y-axis = -3.16583 The coefficient of X variable = 1.669539
The R square, R² is an indicator to evaluate the model-response relationship strength, ranging from 0 to 1. For this analysis, the value is approaching to 1. This shows that the and 𝑦 are estimated to have a quite strong relationship. Apart from that, the standard error of the regression line represents the average distance that the observed values are from the regression line, which is expressed as a percentage. Therefore, the linear regression equation looks such as this:
𝑦 = 1.669539𝑥 − 3.16583 (7) From the equation, it shows that:
ln D = -3.16583 D = 0.042179
𝛼 = 1.669539
The logistic model for the willingness of female private vehicle users to shift to public transport can be present in equation below:
𝑃 = 1
1+0.042179𝑒1.669539𝑥 (8)
From the analysis of result, three main objectives in this study and all of them have been successfully addressed. The main factor that motivates FSSA students to shift to bike sharing system is due to reduced fuel consumption and congestion. Meanwhile the main factor that discourages FSSA students to use bike sharing system is due to hilly topography of UMS as it is very difficult for a student to commuting back and forth by using a bicycle where going up the hill may consume more strength.
All the data related to the willingness of UMS students to shift from private vehicle to bike sharing system have been analyzed. The result shows that 88.1% of respondents which is 141 out of 160 respondents are ready to accept the implementation of bike sharing system. This also indicate that there is still improvement that had to be done in order to increase the willingness of engineering students shift from private vehicle to public transport by implementation of parking charging system. Four transportation model have been developed in the form of logistic model that can reflect the willingness of UMS students to shift from private vehicle to bike sharing system. It is proved that the decrease of bicycle rental charging price able to increase the shift percentage of UMS students from private vehicle to bike sharing system.
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