8 asian academic society international conference

8th ASIAN ACADEMIC SOCIETY INTERNATIONAL CONFERENCE “ASIA Sustainable Innovation: Global Health Diplomacy, Technology and SDGs Accordance”

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INFLUENCE OF THE CAR SHARING SERVICE ON PUBLIC TRANSPORT USAGES

Thanaphon Mathuravech1 and Angkee Sripakagorn1* Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok,

10330, Thailand.1 *Corresponding author, Email address: [email protected]

ABSTRACT

The present study is to study how can Ha:mo usages affect to walking, and public transport by evaluating the autonomy. The study was focused on the long-term users during Oct. to Nov., 2019. Travel distance, velocity, frequency of transport, and fare were collected by exploring the transport situation. The main indicator was the accessibility index calculated by MATLAB. The original mathematical modelling of accessibility index was developed into the implicit function based on travel cost, travel time, and travel modes. The findings showed car sharing service in university could be adopted to connect trips between public transportations. hence, Ha:mo contributed to make more walking, and more convenient transports covering areas. However, still lack of Personal Miles Travel ‘s equations to predict the travel behaviors. The findings can contribute to use car sharing service instead of using public transport. Therefore, this contribution can reduce more traffic congestion, and reduce private cars.

Keywords Car sharing service in university, Ha:mo, Long - term users, Accessibility index and Autonomy. INTRODUCTION

Car sharing is one of the shared vehicle-use services growing rapidly in popularity, often backed by private partner organizations, such as in universities and in urban areas because car sharing can both reduce in parking demands and traffic congestions as well as is believed to have both social and environmental benefits (Martin et al., 2010; Transportation Research Board, 2005) Carsharing on university campus: subsidies, commuter benefits, and their impacts on carsharing. A number of country perceives the majority of advantages in car sharing service, therefore it is often running the business in the universities for supporting the students who are the most usages while the others always use any other transport modes i.e., public transportation, private car. For this reason, car sharing business significantly emphasizes to run the business in the university or is known as Niche market (Zhou, 2014). The niche market can make high profits and easily upscale the strategies of business model (Meijer, Schipper, & Huijben, 2019) therefore the car sharing program operating in the university became more successful than in the urban areas and in the rural areas because the mass market had high competitive market (Zhou, 2014).


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Car sharing in this research is setting up by Chulalongkorn University and TOYOTA Motor Thailand Co., Ltd. in the project name of “CU TOYOTA Ha:mo” for promoting and motivating people to use Car sharing in Thailand. Ha:mo or Harmonious mobility, is the campus electric vehicle sharing system, can reduce using private vehicle and perceive environmental concern. Users can pick up and return Ha:mo in any stations or known as One-way Station based car sharing. The objectives of this research are Objective 1 is to study how can Ha:mo usages affect to walking, and the public transport usages and Objective 2 is to study evaluates the improvement of the autonomy in the transport situation as a result of the implementation of car sharing in university area. The main indicator for the autonomy is the accessibility index calculated by MATLAB. The average velocity, travel distance, fare and frequency of transport are also investigated for evaluating the ability to reach those areas. The results of this work will act as the guidelines to promote car sharing adoption and set up a new transport network in Thailand. LITERATURE REVIEW Car sharing service can be categorized into 2 categories based on parking area: 1) Station-based car sharing. Users can return car only at the same station called Two-way station-based car sharing, while the car is returned at the different station called One-way station-based car sharing (Nourinejad & Roorda, 2015). And 2) Free-floating car sharing. None of the stations are available for free-floating car sharing. Therefore, the available cars are parked into the parking area (Machado, Hue, Berssaneti, & Quintanilha, 2018). In general, people always use combines modes of transport in everyday life or modal shift, but the decision making depends upon cost, access time, waiting time, energy consumption from travel, and congestion. All the ways are different but uniqueness. The car sharing service becomes an important role in transport, and travel which is affected the high competitive market to choose a suitable transport mode itself. However, authors categorize the modal shift in transport modes into 2 sections as follows: (1) Public transit: In the U.S., People can save overall transportation costs by adopting car sharing service because there are several car sharing stations distributed into the numerous areas with cheap costs (Duncan, 2011). Thus, a quantity of public transit demand usages become decreasing (Shaheen & Martin, 2011). On the other hands, if the cost of public transportation isn’t more expensive, people will be able to use public transport more frequently than car sharing service in China (Yoon et al., 2017).(2) Walking: The decision making to walk depending upon context and environment (Cervero, Round, Goldman, & Wu, 1995). If the public bus stops are further away, people will decide to use other transport modes instead (Ker & Ginn, 2003). On the other hands, to use car sharing can also affect to increase trip generations especially in walking and cycling approximately by 26% and 10% respectively, however when comparing with public transportation is decreased by 9% (Shaheen & Martin, 2011). However, the observed relationship between public transit availability, and car sharing was ambiguous. The number of hypothesis in previous studied researches were expected to the role of


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the car sharing service to the public transportation (E. Martin & S. Shahen, 2011: T. Stillwater et al., 2009 and (Nishigaki et al., 2020) A study about the possibility to travel by various transport modes or autonomy travel was limited. One of an interesting study was the evaluation of the improvement’s areas of Ha:mo stations (Nishigaki et al., 2020) by using modified numerical approach (Nguyen & Yoshikawa, 2016). However, still lack of 1) considerations about the increase in the car sharing stations in study area, 2) The relationship between the accessibility index and the inclination of demand usage by other transport modes and Ha:mo in study area. Authors gave an attention to significant in autonomy travel because our study area was an urban area served by full of various transport modes. Car sharing was also served in university environments to offer a service to students, faculty and staff (Zheng et al., 2009). The students have a higher participation rate than university employees (Zhou, 2013). However, the university staff and faculty are more likely to use public transportation or other modes, instead of their car; they need to travel to run errands during their workday often; and their workplace has the characteristics that would make a carsharing program successful. The authors insist on the idea that universities are a niche market for carsharing (Zhou, 2013; Zhou, 2014). Few studies investigated the smart mobility in university campus, but didn’t study the characteristics of car sharing service (L. Rotaris et al., 2019).

In this study, Ha:mo in Figure 1 is a one-way station based car sharing, which have some limitations that affect to the problem of one-way car sharing in the imbalance between the stations in order to high demand usages in the different time and the stations are not distributed covering the overall areas of operating service (Shaheen et al., 2015). These limitations of the one-way car sharing service can affect to the satisfaction of user adoption and to the decision making to use other alternative transport modes and the organization will lose some profits from operating service than expected (Alfian, Rhee, Kang, & Yoon, 2015).

Study Area

Chulalongkorn university is located at the center of Bangkok, Thailand. This area also is a viable economic growth’ areas connected to the Rama I Road (North section), Henri dunant Road (East section), Rama IV Road (South section), Phyathai Road (Middle section), and Banthat thong Road (West section), and also nearby shopping malls i.e. MBK Center Bangkok, Siam Discovery, Siam Paragon, Siam Square, and Central World. Moreover, the number of public transportations can connect to these areas i.e. Public bus, BTS, and MRT making more convenient travel to reach the area easily. Figure 2 illustrated to the study area, and the blue pins are station services.

Transportation Situations

Chulalongkorn university can be accessed by Metropolitan railway both BTS and MRT, also public bus. Both Siam station and National Stadium Station were near Chulalongkorn


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university in the North Section (Rama I Road), While Samyan Station was connected to Chulalongkorn university in the South section, and others can be easily accessed by public bus.

The university provides CU Shuttle Bus is an electric shuttle bus service that cover not only campus area, but also reach out to BTS and MRT around the campus. In addition, CU Bike is a bike sharing program, which quickly grown in popularity among CU students. A new CU TOYOTA Hamo is another great option of green transportations for those who cannot ride a bicycle and for older people of the aging society. MUVMI is a Fist-Last Mile tuktuk sharing service that helps people getting to neither BTS nor MRT, and back. Leaf E-scooter is an electric scooter sharing platform to served members traveling in short distances around university campus. All these projects help promote the development of innovations and practices that are both sustainable and protective of the environment of Chulalongkorn University, as well as the surrounding community.

There are 3 parameters that can be as indicators to assess the transport situation in our study area as follow: 1) Accessibility index (AI) can be defined as the ability to reach any activities, individual, or opportunities by moving to the places where those needs are located (Geurs and Ritsema van Eck, 2001; Handy, 2005). The interpretations of accessibility are estimated by the spatial distribution of the destination, by the ease of reaching it and by the quality and characteristics of the activities encountered (Handy and Niemeier, 1997)., 2) Personal Miles Traveled (PMT) is a standard measure of mobility that combines both the number and length of trips (Federal Highway Administration [FHWA], 2020). PMT, as one of an indicator, is used to correspond between the origin point to the destination point to describe about the relationship in function of significant distances during the some origin point to the destination point., and 3) Autonomy Travel is referred to a several shifts that might affect traveler’s satisfaction with their travel experiences, including more personalized alternative selections, and greater efficacy and efficiency (Kim, Chung, & Lee, 2011; Luszczynska, Gutiérrez-Doña, & Schwarzer, 2005).

Definition of Availability Index

The original mathematical modelling of accessibility index function (Nguyen & Yoshikawa, 2016) was developed by Nishigaki et al. (Nishigaki et al., 2020) into the implicit function based on Travel cost, Travel time, Travel modes, and Energy consumption. Therefore, Accessibility index can be defined as ability to reach the destination by Ha:mo, CU Shuttle bus, Public bus, or Walking, and the interpretation based on the developed implicit functions.

Accessibility index was established by MATLAB coding constituting Without Ha:mo program, and With Ha:mo program. The number of meshes in this study were equivalent to 22x32 meshes.

Equation (1) was defined as the Path cost in scenario S when traveling from mesh i to mesh j on path p where the first term indicated travel time, the second indicated waiting time, and the third term the expected cost. δ_(l,p)^S was one if path P includes link l and 0 otherwise. α was Cost travels where was equal to 2 (Goodwin, 1976), and β was Value of time where the ratio of


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the satisfaction changes in traveling time to the satisfaction changes in Fare. C_(ij,p)^+ was path cost in Walking, Public bus, Ha:mo, and CU Shuttle bus, while C_(ij,p)^- was omitted Ha:mo.

𝐶#$,&' = 𝛿*,&'+,(./

,)1,

+ 𝛼 456+/

, +78𝐹*::∈'* (1)

Equation (2) was defined as the distance cost function.

𝑓: 𝑑*: =0.00489(𝑑*:)6 + 1.0462 𝑑*: ,(𝑘 = 𝑤𝑎𝑙𝑘)𝑑*:,(𝑘 = 𝑃𝑢𝑏𝑙𝑖𝑐𝑏𝑢𝑠, 𝐶𝑈𝑏𝑢𝑠, 𝐻𝑎:𝑚𝑜)

(2)

Equation (3) was defined as the Minimum path cost (𝐴#$' ) where the minimum costs from the origin point to the destination point.

𝐴#$' = 𝑚𝑖𝑛 𝐶#$,&' (3) The accessibility improvement (𝐴𝐼#$' ) due to the presence of Ha:mo can be obtained as in equation (4), and 𝐴𝐼#$' was equal to the difference between the accessibility index in without Ha:mo’ s scenario (𝐴#$X ) and in with Ha:mo’s scenario (𝐴#$Y ).

𝐴𝐼#$ = 𝐴#$X − 𝐴#$Y (4) Equation (5) was defined as the ratio of improvement.

𝐴𝑅#$ =𝐴#$X − 𝐴#$Y

𝐴#$X

(5)

To indicate increases in Personal Miles Traveled can observe from the change in accessibility index. According to Figure 3, the origin point can be at any positions in the study area, then pointed them out to only one destination point. Accessibility index became an embed value in the destination point to interpret it to the ability to access from any origin points to that destination point by other modes of transport based on the developed implicit function. The distance during the origin points to the destination point can be defined as a PMT that can affect to the accessibility index, but it was currently not merged to the implicit functions in this research.

In scenario I illustrated higher value in accessibility index than the another one. It meant that the yellow area can be easier to access the destination point than the orange area because of the factor governed by the implicit functions.


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Scenario I Scenario II Figure 3: Relation between PMT and Accessibility index

In practical view, there were several travel patterns in real transport situation. If traveler usually went to some places, it wouldn’t increase in PMT but it increases in Accumulated PMTs instead. The Accumulated PMTs were then obtained by summing up the personal miles travel in a year (U.S.TransportationDepartment, 2015), however only PMT per a single trip was studied in this research because of the limited time and it was impropriate in the small scaled study’s area.

MATERIAL AND METHODS

The research methodology used in this paper was based on the theorical analysis to evaluate the improvement of the autonomy, extremely lacked of the Global Positioning System (GPS) to predict actual demand patterns and manage imbalance between station, which was based on the empirical data for long-term usages had been collected since during October – November, 2019.

Transport modes in this research considered 4 modes: 1) Walking, 2) Public bus, 3) CU Shuttle bus, and 4) CU TOYOTA Ha:mo. Groups of parameter could be enumerated as follow: Group 1: Overall distances of study area were collected by Mapping toolbox© functions in MATLAB to calculate the distance between two points in geographic space, Group 2: Average velocity of other modes was calculated by the ratio of travel distance to time change, Group 3) Frequency of transport modes per hour was observed from the transport’s situation in 1 hour, and 4) Fare for other modes. MATLAB was used an important tool to make both main program, and sub programs, after all of the parameters had been already collected.

Authors further take the simplifying assumption that all travelers used the shortest path. Some work indicated that the congestion on public transport, and on the roads could be ignored (Nishigaki et al., 2020). In the case of Bangkok, the congestion on the road in this research couldn’t

Accessibility index modification

PMT

PMT

PMT1

PMT

Accessibility index = 56 Destination

Origin

Origin Origin

Accessibility index = 32 Destination

Origin

Origin

Origin PMT PMT


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be ignored because the congestion was the main environment, which can govern to majority of related parameters. The future research should be adapted the spatial mesh to show the Personal Miled Travel (PMT) values by developing the accessibility functions.

RESULT AND DISCUSSION

Looking at the results before the introduction of Ha:mo, Figure 4 showed that, in general, accessibility along the CU Shuttle bus was much better. In contrast, the areas only served by public bus routes, or those far from any public transport had been relatively poor accessibility. This was explainable given the lower service quality of the public buses. Looking at this more details in the Figure 4(a) illustrated more clearly the influence range of the CU Shuttle bus that governed the accessibility network where especially inside the east of university due to the lower waiting time than public bus, and without fare. On the other hands, if some areas were far from either public bus stop, or CU Shuttle bus stop, people would decide to neither walk (if possible) nor use other transport modes. Considering currently the results in the scenario of with Ha:mo in Fig. 4(b), it could be seen that the range of good accessibility had been expanded around the locations where the Ha:mo station had been operational. The good accessibilities around these meshes were clear visible. Furthermore, it was noteworthy that accessibility improved, in Fig. 4(c), a lot and gradually connected each other in meshes during the West side of university – Phayathai Road – East side of university compared to meshes that was close to along public bus stops.

(a) Without Ha:mo (b) With Ha:mo (c) Improvement

Figure 4. Resulting in accessibility index (22 stations). In 2020, Ha:mo station was increased by 2 stations (Stadium One station and Samyan

Mitrtown station). Figure 5(b) illustrated that the influence range was expanded and more connected the both side of the Chulalongkorn University In addition, Figure 5(c) illustrated that the accessibilities were better improved a lot compared to Fig. 4(c).


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Figure 5. Resulting in Accessibility Index (24 stations)

Considering the ratio of 22 stations’ s improvements in Fig. 6(a), and 24 stations’ s

improvements in Fig. 6(b) respectively, The results showed that the ratio improvement in 24 stations were widely more connected the overall areas than the 22 stations because the Stadium One station and Samyan Mitrtown station were added into the areas that didn’t connect to other transport modes. The comparison of the ratio of some effective improvements both East and West section was showed in Table 1, and Table 2 respectively. According to Table 1 and Table 2, most of the adjacent area characteristics, which were near to the connected public transport hub (i.e. BTS green line, Public transport bus, and MRT Blue line), were higher in the ratio of improvements because anyone can adopted Ha:mo connected to the public transport or other transport modes conveniently. Improvement areas are effectively increased approximately +13.79% to +48.84%, compared between the highest ratio of improvements value of 22 and 24 stations, in east section areas because a number of Ha:mo stations were mostly inside the buildings, and near public transport hub. Otherwise, the projections in west section also were effectively increased approximately higher than +50% because the stations were mostly more distributed, and nearer transport hub than the east section. Table 1: Compared the ratio of improvements 22 stations to 24 stations at East section of university campus

Areas 22 Stations 24 Stations Characteristics Faculty of Veterinary Sciences 31% - 58% 41% - 66% Stations are

inside areas, and near

transport hub.

Chulalongkorn University Auditorium 21% - 43% 37% - 64% Faculty of Engineering 20% - 67% 56% - 73% Faculty of Commerce and Accountancy 27% - 66% 58% - 74%

(c) Improvement


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Table 2: Compared the ratio of improvements 22 stations to 24 stations at West section of university campus

Areas 22 Stations 24 Stations Characteristics Stadium One 24% - 39% 52% - 73%

Stations are only around areas, and

near transport hub.

The National Stadium of Thailand 24% - 39% 42% - 69% Chulalongkorn 44 Avenue 21% - 45% 56% - 67% Samyan Mitrtown 19% - 45% 48% - 71%

The results can answer the objectives as follow: Answer for Objective 1 This study also

demonstrated that car sharing service could be adopted to connect trips between public transportations. hence, Ha:mo contributed to make more walking, and use more public transports as a supporter. Answer for Objective 2 Ha:mo can increase autonomy, and made more convenient transports covering areas inside university. This study about one-way car sharing service in university was new service that operated only CU. Moreover, the accessibility function was also new to study in transport research and can be adopted to the future works. Researcher suggested to expand the areas of transport research to the Personal Miled Travel (PMT) using adapted accessibility function. However, still lack of PMT equations to define parameters, and to predict the travel behaviors. These research findings can contribute to the societal problems by emphasize on supporting to use car sharing service instead of using public transport, or private car. Therefore, this contribution can reduce more traffic congestion, and reduce private cars.

CONCLUSION

CU TOYOTA Ha:mo has been operational since 2017 until now, We obtained findings to improve the quality services as follow: 1) The existed Ha:mo stations can increase the ability to reach the overall areas., 2) To add more Ha:mo stations can affect to increase more ability to reach the destinations not only near by the stations, but also the overall study area., 3) Autonomy travel can also be increased the several modal shifts that might affect traveler’s high satisfaction by Ha:mo usages. Furthermore, the more Ha:mo stations were added, the more autonomy travel was increased., 4) Ha:mo can attract in more walking, but Ha:mo can decrease in public transportation usages. and 5) Both waiting time, and fare are one of the important factors that affect to the decision making in use any transportation modes. REFERENCES Alfian, G., Rhee, J., Kang, Y.-S., & Yoon, B. (2015). Performance Comparison of Reservation

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