Using Ordered Probit Modeling to Assess Perceived Bus Performance

Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015

Using Ordered Probit Modeling to Assess Perceived Bus Performance in Phnom Penh Veng Kheang PHUNa, Pharinet PHENGb, Tetsuo YAIc a

Institute for Transport Policy Studies, Tokyo, 105-0001, Japan; E-mail: [email protected] b Department of Public Work and Transport, Phnom Penh, Cambodia; E-mail: [email protected] c Department of Built Environment, Tokyo Institute of Technology, Yokohama, 226-8502, Japan; E-mail: [email protected]

Abstract: After the first and fail attempt in 2001, the public bus service is now provided for citizens of Phnom Penh. However, the sustainability of the public bus service remains questionable due to unstable demand. This paper assesses the public bus performance from the viewpoints of bus passengers. Factors affecting passengers’ perceived bus performance are investigated under an ordered probit model, using data from an on-board survey with bus passengers during service testing period in Phnom Penh (N = 1,100). Results show that the perceived public bus performance is likely to be improved by enhancing the bus attributes (e.g., speed, comfort) and by addressing the concerns expressed by the bus passengers (e.g., requests for bus service expansion). The findings provide the government useful information in considering appropriate strategies to ensure the sustainability of the public bus and to lessen the current traffic issues in the city. Keywords: Ordered probit model, Phnom Penh, Public bus performance, Sustainability

1. INTRODUCTION Without a formal public transport mode in Phnom Penh, the capital city of Cambodia, citizens often experience critical traffic issues such as congestion, accidents, and air pollution (Neth et al., 2005; Kov and Yai, 2009; Long et al., 2011a). After the civil war and Khmer Rouge Regime in the 1970s, major transport infrastructures in the city were almost all destroyed. Developing from that period, the total road length available for general traffic in the city is 1,380 km (JICA, 2013). Due to the rapid economic growth, Phnom Penh is now home for more than two million people. This has resulted in increased motorized vehicles, more than 3.7 times compared to the number of vehicles registered in 2000. This has also caused a large urban mobility, making the available road capacity at several major road sections lower than the traffic demand and hence, resulting in a serious traffic congestion. The dominant modes of transportation in the city are motorcycles, constituting approximately 75.0% of the traffic composition in Phnom Penh (JICA, 2013) and approximately 86.0% of all registered vehicles in Cambodia (ASEAN-JTSB, 2013). Motorcyclists are at high risk because they ply on the streets without what so-called “protective shell” (WHO, 2009). Approximately 70.0% of the road fatalities (80.0% of the casualties) occurred among motorcyclists in the city (Kov and Yai, 2011), and it is equivalent to about 66.0% in the countrywide (Bachani et al., 2013). Accordingly, the traffic law has 

Corresponding author.

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been revised. The traffic enforcement and road safety programs have also been implemented as countermeasures against the current traffic accidents. Further, the concentration of Polycyclic Aromatic Hydrocarbons (PAHs) in Phnom Penh was extremely high; it is about six times higher than PAHs in Bangkok, Thailand (Furuuchi et al., 2005). Yet, there is no particular action to improve the air quality in Phnom Penh. Although there were some discussions on cleaner fuels and vehicle inspection & maintenance, the mobile emission standards have not been set (ADB, 2006). Moreover, the traffic issues are the consequences of inadequate policies & regulations (e.g., traffic regulation and vehicle growth control), inefficient traffic management (e.g., traffic control and signalization), insufficient infrastructures (e.g., parking facility, drainage, and sidewalk), and particularly the absence of urban public transport system. Citizens predominantly commute by informal public transport (paratransit) modes such as Motodop (motorcycle taxi), Remork (motorcycle with a twowheeled carriage), and Cyclo (pedal-powered tricycle), because these modes provide a doorto-door transport service with shorter travel time and an affordable fare level (Phun and Yai, 2015). The traffic issues in Phnom Penh have become a common social problem, which has decelerated the economic activities, intensified the travel cost and time, and degraded the quality of life. To tackle this problem, the government has considered the formal public transport modes such as intra-city public bus and urban rail systems including Bus Rapid Transit (BRT), Light Rail Transit (LRT), Skyrail, and Tramway (JICA, 2001; JETRO, 2009; SYSTRA, 2012). Among these modes, only the public bus service was actually introduced in Phnom Penh. For a given mass transit system, one of the key issues for transit engineers, planners, and operators is how to enhance the service quality (or system performance) that is acceptable by transit riders (Choocharukul, 2004). Up to now, there are only few studies focusing on the planning issues of the public transport system in Phnom Penh. Motivated by an absence of a formal public transport mode in the city, Choocharukul and Ung (2011) conducted a stated preference study with general commuters to understand the potential mode change, from private vehicles and paratransit modes to a public bus. Based on several levels of bus service attributes including bus fare and headway, it was found that the potential demand for public bus service was remarkably high. Long et al. (2011b) explored the psychological factors influencing on commuters’ behavioral intention toward the usage of future sky train in Phnom Penh. Lon et al. (2013) examined the preferential choices among private vehicles (motorcycle and car) and public transport (BRT and LRT). Nevertheless, these studies appealed for further researches (e.g., the feasibility of the transit service) in order to plan for an efficient mass transit system in Phnom Penh. Despite the first and fail attempt of the public bus service in 2001, Phnom Penh Capital Hall and Japan for International Cooperation Agency (JICA) brought back the public bus service in early 2014. After a one-month long experiment, the bus service was extended and service routes were expanded. The share of public transport in Phnom Penh was approximately 15.0% in 2001 and was planned to be more than 30.0% in 2035 (JICA, 2013). The bus service is still quite new and the fare is relatively cheap (i.e., a flat fare of 1,500 KHR ≅ 0.37 US$ per trip, as exchange rate of February 2014). The public bus was introduced, but there are insufficient infrastructures to support its smooth operation. However, the sustainability of the public bus service in the city is uncertain, mainly due to an unstable passenger demand. Therefore, there is a need to examine the overall service quality provided by recent public bus in the city. This paper assesses the public bus performance in Phnom Penh from the viewpoints of bus passengers. The public bus performance refers to the transit service quality such as

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comfort & safety inside the bus, travel time, the convenience of the service, and the existence of supporting infrastructures (Dell’Olio et al., 2010). We examine the fundamental issues that passengers took into account when they perceive the service quality of public bus in the city. The perceived bus performance can be the most suitable indicator for assessing the service quality, experienced by the passengers (the real consumers of the public bus service). The factors significantly influencing on perceived bus performance are investigated under an ordered probit modeling approach. The findings of this study are expected to provide some insights into strategic planning for sustaining the public bus service by increasing demand of bus passengers and reducing the current traffic issues in Phnom Penh. Several aspects of public bus services such as perceived service quality have been investigated in several Asian developing cities (e.g., Guarino et al., 2001; Anh et al., 2005; Rahman and Nahrin, 2012), but there is no known study for Phnom Penh in Cambodia. In addition, the ordered probit modeling, among other analysis technics, has been proven useful in studying the (perceived) service quality of mass transit and the factors affecting transit ridership (Nkurunziza et al., 2012). Most of these studies investigated the service quality in relation to transit & service attributes and passengers’ characteristics (e.g., Abdel-Aty, 2001; Choocharukul, 2004; Dell’Olio et al., 2010; Antoniou and Tyrinopoulos, 2013). Yet, little research has incorporated the passengers’ free opinions (e.g., comments and requests towards improvements on public transport system) into the ordered probit modeling. In this paper, we adopt the ordered probit modeling that considers not only the bus attributes but also the passengers’ characteristics and the free opinions regarding the recent introduced public bus service in Phnom Penh. Particularly, the efforts towards the sustainability of the public bus will be better off when passengers’ concerns are included in the menu of bus service improvements.

2. PUBLIC BUS SERVICE 2.1 First Bus Experiment With assistance from JICA, a one-month public bus service was first implemented in June 2001 in Phnom Penh as an experiment (JICA, 2001). The main objectives of the public bus experiment were to help citizens to realize the merit of the bus system, identify the potential effects of bus service, and recommend strategic solutions for the planning issues of the bus service in the city. Buses were operated along two routes: Route 1 lied from north (CambodiaJapan Bridge) to south (Chbar Ampov market) along Monivong Boulevard (about 8.5 km) and route 2 was a circular line centered in the city (about 8.5 km). Table 1 shows the outline of the public bus experiments in Phnom Penh. There were 56 bus stops with 300 m – 500 m space between, and only eight of them had shelters and seats. To facilitate the bus operation, some regulations included prohibition of on-street parking and 2-wheelers at some segments of bus routes were imposed. The total number of passengers during the trial period of bus service was 103,329 (Route 1 = 60,276 and Route 2 = 42,963). Due to the substantial public supports, the public bus service was extended and operated by Department of Public Work and Transport (DPWT) with a fleet of 17 buses and two flat fare levels. The flat fare of 500 KHR (≅ 0.13 US$ per trip, as exchange rate of June 2001) was implemented during the first 5 days and the last 8 days of trial period. The fare of 800 KHR was implemented during 6-22 June 2001. However, the public bus service was ended after a month extension, mainly due to the financial deficit.

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Table 1 Outline of the public bus experiments in Phnom Penh Items Experiment period Bus routes (length) Operation hours Bus fleet (capacity) Bus stops and space Number of shelters Bus frequency Average speed Total passengers Bus fares and daily average passengers per day: Service extension

First bus experiment 1-30 June 2001 Route 1 (8.5 km), Route 2 (8.5 km) 5:30-19:30 23 air-conditioned minibuses (29 seats) 56 stops and every 300-500 m 8 Every 6-10 minutes Route 1: 13.9 km/h, Route 2: 13.1 km/h Route 1: 60276, Route 2: 42963 500 KHR 800 KHR Average Route 1: 2668 2019 2344 Route 2: 1661 1077 1369 1 month (17 buses and 2 routes)

Second bus experiment 5 February-4 March 2014 Line 1 (7.5 km) 5:30-20:30 10 air-conditioned buses (35 seats) 36 stops and every 250-660 m 5 Every 10-15 minutes 9.9 km/h 43278 1500 KHR Line 1: 1546 Till present (43 buses and 3 lines)

Note: Information in this table was extracted from JICA (2001) and Phnom Penh Capital Hall The term “Route” or “Line” here has equivalent meaning. Each term simply represents the bus operating route for the different timing of bus experiments.

2.2 Second Bus Experiment With a belief that public transport is the most efficient way to solve the current traffic issues in Phnom Penh, the municipality and JICA again brought back the public bus service in early February 2014, with three purposes: 1) to give an opportunity for citizens to experience the comfort and safety travel, 2) to set the stage for a continuous bus operation based on the accumulation of bus operation know-how, and 3) to comprehensively develop the urban transport system in the city (JICA, 2013). During the period of second bus experiment, ten air-conditioned buses (non-standard bus with one door) were deployed along the Monivong Boulevard (Line 1), from Old Stadium Roundabout to Chbar Ampov market (about 7.5 km). The bus priority signal system was implemented at three major intersections to facilitate the bus operation. With a portable GPS device we equipped onboard, the average bus operating speed was measured to be 9.9 km/h, which was relatively slow. The slow bus operating speed was mainly due to the increase in motorized vehicles in the city, particularly along the bus route. The general traffic volume was increased from 99,389 in 2000 to 133,328 in 2012 (about 34.0%), and consequently the average travel speed was reduced from 22.1 km/h in 2001 to 15.0 km/h in 2012 (JICA, 2001 & 2013). 2.3 Bus Service Afterward Although the average number of daily bus passengers was lower than that in 2001 (i.e., Route 1 = 2,344 > Line 1 = 1,546), after a month of the second bus experiment, the public bus service was extended and operated by Phnom Penh Capital Hall and DPWT. Until early September 2014, the bus route (Line 1) was expanded from 7.5 km to 19.0 km in both directions. In addition, two more bus routes (Line 2 and Line 3) were added with a total fleet of 43 buses (40 standard buses with two doors and three minibuses), see Figure 1. The bus service is still quite new and the flat fare is relatively cheap, only 1,500 KHR (0.37 US$) per trip. Most of the bus stops are on the roadsides that have been labeled as “Bus stop,” with lane marking and station pole. The public bus services provided along major roads (Line 1 = 19.0 km, Line 2 = 19.0 km, and Line 3 = 13.5 km) in Phnom Penh would make up just a fraction of the existing road network and is likely to have little charm for general citizens as large number of commuters still prefer a door-to-door trip.

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Koh Dach

Legend

N

Bus Line 1 (Feb.-Sept. 2014) Bus Line 1 (Sept. 2014 ~) Bus Line 2 (Sept. 2014 ~) Bus Line 3 (Sept. 2014 ~) Bus terminal National road Phnom Penh boundary District boundary

Kilometer 9 Line 1 = 19.0 Km

Chroy Changvar

Night Market

Line 3 = 13.5 Km PNH Airport

Mekong river

Boeung Chhouk

Chom Chau

Line 2 = 19.0 Km

Ta Kmau

Figure 1 The operating routes of public bus in Phnom Penh The operation of the public bus was later transferred to a Phnom Penh City Bus authority, which is temporarily established to manage the bus operation. Several more shelters along the bus lines are constructed and some regulations such as on-street parking laws are enhanced to facilitate the bus operation. Based on Phnom Penh Capital Hall, the demand trend is observed to be gradually upward (Figure 2). Prior 10 October 2014, the average numbers of daily bus passengers were Line 1 = 1,148, Line 2 = 609, and Line 3 = 763 (Total = 2,521 passengers per day in average). Although the daily passenger demands remained relatively low, the government appeared to have stronger supports for the public bus service in Phnom Penh. 8000 Daily passengers

7000 6000 5000

Line1 Line2 Line3 Total FreePax

4000 3000 2000 1000 0 15-Sep 5-Oct 25-Oct 14-Nov 4-Dec 24-Dec 13-Jan 2-Feb 2015 2014 Date

Figure 2 Number of daily bus passengers from 15 Sep. 2014 to 3 Feb. 2015 Based on the experience with fare strategy in 2001, the government launched a special fare discount after 10 October 2014. The bus service is free of charge for all students with a valid student ID card, children with the height of one meter or less, elderly with age of 70 or

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higher, Buddhism monks, and the disabled. With this fare discount policy, the average numbers of daily bus passengers were substantially increased to Line 1 = 2,025, Line = 1,524, and Line 3 = 2,479 (Total = 6,029 passengers per day in average). The shared percentages of passengers with free of charge were approximately Line 1 = 40.9%, Line 2 = 43.0%, and Line 3= 59.1%. Although Line 3 appeared to have attracted more passengers than Line 1 after the implementation of fare discount policy, Line 3 had also higher share of passengers with free of charge. The daily average number of passengers for all bus lines was 6,209, but 40.4% (= 2,434 passengers) of them were free riders. This proportion has led to a concern on the sustainability of the bus service in term of its profitability. To this end, it would be useful to assess the public bus performance from the viewpoints of bus passengers in order to improve the bus service quality and increase the passenger demand in the near future.

3. SAMPLE 3.1 Interview Survey This study is based on data obtained from an interview survey during the period of second bus experiment in Phnom Penh, 5-11 February 2014. Eight interviewers, who were trained to completely understand the survey questionnaire, conducted the interview survey on-board with 1,100 bus passengers, of which 547 were females. Collected information included personal characteristics (age, gender, and occupation), trip purposes, previous travel modes before the public bus service became available, access and egress distance to/from a bus stop, the subjective evaluation scores on overall bus service quality (bus performance) and bus attributes (operating speed, fare, safety, and comfort) using a 5-point scale (1: very bad, 2: bad, 3: medium, 4: good, and 5: very good), and the passengers’ free opinions regarding the public bus service. 3.2 Respondent Characteristics The characteristics of interviewed passengers are reported in Table 2. Majority of respondents (41.5%) were staffs or workers of a company or government. Students accounted for 30.5% and majority of them (88.1%) aged between 15 and 29 years old. Respondents had various trip purposes; 25.2% of them were heading home, 17.2% was going to a workplace, 16.9% was about to visit a friend or relative, and 8.8% was travelling to a school. Interestingly, 9.4% of respondents came to test riding the public bus because they wanted to experience it. The shares of respondents for each access and egress range are akin. Approximately 41.5% = (41.2% + 41.8%) / 2 of the respondents had access/egress distance shorter than 100 m. Majority of respondents with this access/egress distance were students (42.3%) and staffs (31.8%). Furthermore, we observed a high share of respondents who had access/egress distance longer than 400 m (approximately 32.5%). Approximately 49.7% of these respondents were staffs, and 21.9% were students. The high share of longer egress distance is not a surprise because there was only one single bus route with limited length (7.5 km) in the city, during the second bus experiment period. 3.3 Modal Shift The introduction of public bus service has attracted substantial amount of private vehicle users. The information on previous modes shows that 66.3% of the interviewed passengers

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previously travelled by their own vehicles (54.8% by motorcycle and 11.5% by car), 30.4% used paratransit services (22.4% by Motodop, 7.2% by Remork, and 0.8% by taxi), and 3.3% rode a bicycle or was on foot. Although the majority (66.3%) claimed that they previously travelled with own vehicles, this does not mean that they have completely switched from private vehicles to the public bus. Some of them may previously commute along the bus route with a driver (parents, relative, or a friend). After the bus service became available, the driver may drop them off or pick them up at a bus stop. As evidenced by the high share of respondents who had access/egress distance longer than 400 m (Table 2), the respondents were likely to have the travel modes switched rather than walking to/from the bus stop. In addition, some 9.4% of respondents who came to test the public bus service might not continue to use it. With this regard, we seek for a more reliable share of modal shift among bus passengers. Table 2 Respondent characteristics (N = 1,100) Variable Gender Male Female Occupation Student Staff Self-employed Housewife Other Age ≤ 14 15-19 20-29 30-39 40-49 50-59 ≥ 60 Access distance (m) ≤ 100 101-200 201-300 301-400 ≥ 401

Percentage

30.5% 41.5% 12.8% 9.1% 6.1%

Variable Trip purpose School Work Business Shopping Entertainment Home Visit someone Test riding bus Other

3.2% 14.3% 28.5% 20.9% 11.4% 12.5% 9.3%

Previous travel mode Own car Motorcycle Motodop Remork Taxi Bicycle Walking

11.5% 54.8% 22.4% 7.2% 0.8% 1.6% 1.7%

41.2% 15.7% 7.1% 5.0% 31.0%

Egress distance (m) ≤ 100 101-200 201-300 301-400 ≥ 401

41.8% 11.5% 7.8% 5.0% 33.9%

50.3% 49.7%

Percentage 8.8% 17.2% 3.5% 11.4% 6.5% 25.2% 16.9% 9.4% 1.2%

Since we do not have sufficient information for identifying those respondents who completely switched from private vehicles to the public bus, we use the information on access and egress distance as control variables. We assumed that the maximum walking distance for Phnom Penh’s citizens is less than 200 m; and within this access/egress distance, the passengers would walk to/from a bus stop without using other means of transportation. It was shown that 38.6% of respondents who previously travelled by own vehicles (31.8% by motorcycles and 6.8% by cars) had access/egress distance shorter than 200 m; and this share became 33.1% (27.1% of motorcycle and 6.0% of owners) when we excluded those respondents with “Test riding bus” as trip purpose. This might imply that approximately onethird (33.1%) of bus passengers were completely switched from private vehicles to public bus

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service (i.e., there is reduction in total number of private vehicles on along the bus route). For longer walking distance than 200 m, 33.2% (= 66.3% − 33.1%) of bus passengers were likely to continue using different access/egress modes (i.e., passengers of own vehicles) in order for them to arrive at a bus stop/destination. Therefore, shares of bus passengers were 33.1% who switched from private vehicles (own car or motorcycle), 33.2% who uses access/egress modes (passengers of own vehicles), 30.4% who shifted from paratransit modes (Motodop, Remork, and Taxi), and 3.3% who changed from non-motorized modes (bicycle or walk). However, these shares might have changed following the service route expansion and the fare discount policy in the current bus operation.

4. MODELING METHODOLOGY 4.1 Model The overall public bus performance (i.e., dependent variable) in Phnom Penh was evaluated by passengers in an ordinal category, the 5-point scale (1: very bad, 2: bad, 3: medium, 4: good, 5: very good). The ordered probit model is adopted here because the dependent variable takes more than two values with a natural ordering (Abdel-Aty, 2001). The model can account for the unequal difference among the ordinal categories in the dependent variable (Sadri et al., 2013). Although the outcome is discrete, other methods such multinomial logit model or ordinary least square regression would fail to account for the ordinal nature of the dependent variable. Here, the ordered probit model captures the qualitative differences between different evaluation scores on bus performance. The dependent variable is unobservable and its index model is written as:

yn *   xn   n where, yn* xn β εn

(1)

: the bus performance perceived by an individual n, coded as 0, 1, 2, 3, and 4, : the vector of explanatory variables for an individual n, : the vector of coefficient parameters to be estimated, and : the error terms, assumed to be normally distributed with zero mean and unit variance.

The ordered probit technique will use the observations on yn, which are a form of censored data on yn*, to fit the parameter vector β. Given a set of explanatory variable xn, the bus performance yn falls in a category j if μj-1 < yn* ≤ μj, where j ∈ J = {1, 2, 3, 4, 5}. The bus performance data, yn, are related to the underlying unobservable yn*, through thresholds μj. yn* is unobservable and we only know when it crosses thresholds μj. Although y* is coded as 0, 1, 2, 3, and 4, the difference between the first and second outcome may not be the same as between the second and third. The threshold value between the lowest and the next lowest categories are always normalized to μ1 = 0 (Abdel-Aty, 2001). Because a shift in the intercept (the constant term) cannot be distinguished from a shift in the threshold values, one of the thresholds is not identified. We could only estimate the constant term and three threshold values in the 5-category case. To this end, the observed dependent variable that represents the bus performance category can be expressed in Eq. (2).

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1 2  yn  3 4  5

if yn *  0

(Very bad bus performance)

if 0  yn *  2

(Bad bus performance)

if 2  yn *  3

(Moderate bus performance)

if 3  yn *  4

(Good bus performance)

if 4  yn *

(Very good bus performance)

(2)

The probability of the bus performance yn to fall in a category j ∈ J is given by Eq. (3). pn ( yn  j )  F (  j  xn )  F (  j 1  xn )

(3)

where, pn (yn = j) : the probability that individual n evaluates the bus performance into a category j, F (.) : the cumulative normal distribution function, and μj and μj-1 : the upper and lower threshold parameters for category j The corresponding log-likelihood function can be defined based on the sum of individual log probability in Eq. (4). N

LL( )   log p n ( yn  j ), j  J

(4)

n 1

The ordered probit model will estimate two sets of parameters: 1) the constant term and other threshold values (μ2, μ3, and μ4) that indicate the range of the normal distribution associated with specific values of the explanatory variables, and 2) the coefficient parameters (β) that indicate the relative importance of each explanatory variable in determining the associated likelihood of passengers to evaluate a better performance of the public bus. Further, the marginal effects of a change in an explanatory variable on the probability of perceiving a bus performance category can be derived based on 𝜕p/𝜕x. The sum of the marginal effects for each explanatory variable equals to zero. One should be careful when interpreting the signs of the estimated coefficients in the ordered probit model because they might have different effects on the probabilities of each category of the bus performance. It should be noted that a structural equation modeling might work similarly well for the study of the perceived service quality of a transit service (e.g., De Oña et al., 2013), but this study prefers the ordered probit modeling due to the available data. 4.2 Variables of the Model We excluded those respondents who came to test riding the bus (9.4%), because they did not have any specific trip purpose and their judgments on bus performance might not reflect the viewpoints of general commuters. Thus, the available data for the analysis is N = 997. The descriptive statistics of the variables for the ordered probit model are shown in Table 3. The average score of the overall evaluation on bus performance was high (4.34). While the lowest score was “2: bad,” it was observed that majority of the respondents rated on the “4: good” (55.9%) and “5: very good” (39.0%) of the bus performance categories. These portions indicate that the city bus performed remarkably well during the first month of experiment. In addition, the average scores of the subjective evaluation on bus operating

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speed, fare, safety, comfort, and protection from weather were higher than “3: medium”. This implies that respondents gave a high score on bus attributes when they used the public bus service. This is not a surprise because respondents of this study were bus passengers who would already expect a desired service quality provided. Table 3 Descriptive statistics of variables of the model (N = 997) Variable Description Mean SD Min Max Dependent variable (5-poin scale) y Overall evaluation on bus 4.34 0.57 2 5 performance Personal characteristics Age Age ranges of respondents 4.02 1.61 1 7 Gender 1: female, 0: otherwise 0.50 0.50 0 1 D_Student 1: student, 0: otherwise 0.29 0.45 0 1 D_Staff 1: staff/worker, 0: otherwise 0.43 0.50 0 1 D_Self-employed 1: self-employed, 0: otherwise 0.13 0.34 0 1 D_Housewife 1: housewife, 0: otherwise 0.09 0.29 0 1 D_Other-occupation 1: other occupation, 0: otherwise 0.06 0.23 0 1 Previous travel modes D_Own-car 1: own car, 0: otherwise 0.11 0.32 0 1 D_Motorcycle 1: own motorcycle, 0: otherwise 0.53 0.50 0 1 D_Motodop 1: Motodop, 0: otherwise 0.23 0.42 0 1 D_Remork 1: Remork, 0: otherwise 0.08 0.27 0 1 D_Taxi 1: taxi, 0: otherwise 0.01 0.09 0 1 D_Other-mode 1: bicycle or on foot, 0: otherwise 0.03 0.18 0 1 Access and egress distance Access Access ranges of home-bus stop 2.74 1.73 1 5 Egress Egress ranges of bus stop-destination 2.84 1.78 1 5 Subjective evaluation scores on bus attributes (5-point scale) Speed Evaluation on bus operating speed 3.78 0.80 1 5 Fare Evaluation on bus ticket fare 3.98 0.72 2 5 Safety Evaluation on safety of using bus 4.55 0.53 3 5 Comfort Evaluation on comfort of using bus 4.49 0.59 2 5 Weather Evaluation on weather protection 4.54 0.54 2 5 (rain and sunshine) of using bus Free opinions D_Support 1: expressing supports for bus 0.29 0.45 0 1 service, 0: otherwise D_Expand 1: requesting for bus expansion, 0: 0.48 0.50 0 1 otherwise D_Support × D_Expand 1: supporting bus service and 0.14 0.35 0 1 requesting its expansion, 0: otherwise D_Improve 1: requesting for bus service 0.35 0.48 0 1 improvements, 0: otherwise

Dummy variables were defined for occupation and previous travel modes of each respondent. These variables equal 1 indicating the presence of a quality or an attribute of the

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concerned explanatory variables (e.g., D_Staff and D_Own-car), and equal 0 otherwise. The ranges of age, access, egress were defined based on their classes presented in Table 2. During the interview survey, respondents were also requested to report their free opinions regarding the bus services and operations they were experiencing. Although there was a variety of opinions, their responses were classified into three main groups: 1) Supporting the bus service (e.g., I want the bus to operate permanently, I want more people to use the bus, good travel with safety and low time & cost), 2) Expanding the bus service to other routes (e.g., please operate on all major roads in the city, bus route should be expanded to Ta Kmau city), and 3) Requesting for an improvement on bus service (e.g., fixed time scheduling, morality of drivers and staffs, proper bus stops with shelters and seats, discount for student and elderly). The share percentages of these groups were 29.2%, 47.4%, and 34.9%, respectively. Also, some of respondents did not provide any opinion (15.3%). The sum of these share percentages is not necessary to be 100%, because each respondent could have several free opinions, which possibly fall into different groups of classification. For the model, we defined four dummy variables, three of which correspond to the three classified groups, respectively (i.e., D_Support, D_Expand, and D_Improve). Another dummy variable is the interaction between D_Support and D_Expand. This variable (D_Support × D_Expand) represents the respondents (14.4%) who requested for extension of the bus service and simultaneously suggested for service route expansion.

5. RESULTS 5.1 Estimate Results During the model estimation process, five sets of explanatory variables were entered: personal characteristics, previous travel modes, access and egress distance, subjective evaluation on bus attributes, and the classified free opinions reported by passengers. The estimate results for our best selected model are provided in Table 4. Many of the included explanatory variables are statistically significant at 95% confidential level. The overall model’s goodness-of-fit is acceptable (Pseudo R-square = 0.2074) for identifying the factors influencing on the evaluation of the public bus performance. The threshold values (μ3 and μ4) are highly significant and different from each other, showing that some of five categories of bus performance should not be combined into one. There are only two threshold values because the responses of bus performance fall into only four categories (Table 3). Except D-Student, the other included dummy variables representing passengers’ occupation (i.e., staff, self-employed, and housewife) had significantly negative coefficients, implying that bus passengers with these occupations were less likely to give a “very good” score on the public bus performance in Phnom Penh. Because passengers with these occupational background were frequent commuters (e.g., staffs commute to workplaces, housewives commute to markets, and they return home), they would deeply considered the bus service quality and its related attributes prior evaluating on the bus performance. It should be noted that one of dummy variables (e.g., D_Other-occupation) was not included in the model because it was treated as a benchmark category. Age and gender did not have effects on passengers’ evaluation on bus performance. Regarding the previous travel modes, results showed that former motorcyclists traveling either by own motorcycles or Motodop had significantly negative effects on the likelihood of the best bus performance. One of the possible reasons is that passengers who previously

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traveled by a motorcycle may not be much satisfied with the bus service quality, as they already adapted to flexible transport service and shorter travel time. In addition, the magnitude of D_Motodop is slightly lower than that of D_Motorcycle. This may associate with the travel costs; the travel cost by a Motodop is higher than that by own motorcycle, and both are higher than the travel cost by the public bus. Passengers might have compared the travel time and cost of the public bus with their previous modes. Thus former Motodop riders were a little more satisfied with the public bus service than previous motorcyclists. Passengers with other types of previous modes had no influence on their perceived bus performance. Table 4 Estimate results Variable Personal characteristics Age Gender D_Student D_Staff D_Self-employed D_Housewife Previous travel modes D_Own-car D_Motorcycle D_Motodop D_Remork D_Taxi Access and egress distance Access Egress Subjective evaluation on bus attributes Speed Fare Safety Comfort Free opinions D_Support D_Expand D_Support × D_Expand Thresholds Constant μ3 μ4 Summary statistics Observation = Wald chi-square (20) = p > chi-square = Log pseudo-likelihood = Pseudo R-square =

Parameter −0.0282 (0.0378) 0.0998 (0.0863) −0.2583 (0.2275) −0.4137**(0.1919) −0.3956* (0.2096) −0.5727**(0.2244) −0.1849 (0.2132) −0.4304**(0.1750) −0.3736**(0.1905) −0.0444 (0.2284) −0.9418 (0.6945) −0.0054 (0.0240) 0.0567**(0.0235) 0.3597**(0.0692) 0.3010**(0.0721) 0.6139**(0.1013) 0.2060**(0.0924) 0.4688**(0.1281) 0.2120**(0.0955) −0.6849**(0.1776) −2.0248**(0.5536) 1.6914**(0.2938) 4.1036**(0.3049) 997 297.01 0.0000 −671.25 0.2074

Note: Robust standard errors are in italics enclosed in parentheses. Variable descriptions are shown in Table 3. *p < 0.1, **p < 0.05

The access distance had a negative coefficient, but was not statistically significant. The egress distance was significant with a positive sign, implying that a better bus performance

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category was likely to be evaluated by passengers with longer egress distance. One possible explanation is that a number of passengers may satisfy with the introduction of the public bus service in Phnom Penh, despite traveling long distance from a bus stop to a destination or home. At least, the public bus service could help reducing the travel cost in the city for passengers living in the suburban areas. Further, the magnitudes of access and egress coefficients were substantially small, indicating that providing a shorter access or egress distance may not be a priority option for achieving the best bus performance from the viewpoints of bus users. The included variables representing the evaluation scores on bus attributes (i.e., speed, fare, safety, and comfort) are all positively significant. This indicates that passengers with higher scores for these bus attributes were likely to have higher satisfaction on the public bus service. The results also suggest that any improvements on the bus attributes would result in the best bus performance being perceived by its potential passengers, and hence increasing the demand for public bus service. Moreover, the estimated coefficient of safety had the highest value among other bus attributes, implying that the road safety concern was one of the primary reasons why passengers decided to commute by the public bus. The defined dummy variables representing the passengers’ classified free opinions about the public bus service are statistically significant. The positive coefficient of D_Support suggests that passengers who solely supported/requested for extension of the public bus service were more likely to perceive the bus performance as “very good.” This is true because passengers who liked the public bus service already had a positive image on it. A similar explanation can be said for the passengers who requested for the bus service expansion (D_Expand); for instance, to cover their living areas or desired destinations. The data show that the majority (73.3%) of those requested for bus service expansion were staffs and students (35.0% of the interviewed passengers), who would demand for bus service to cover their homes, workplaces, and schools. However, the probability of the best bus performance was less likely to be achieved for passengers who simultaneously supported the public bus service and suggested for its service expansion, as evidenced by the negative coefficient of the interactive dummy variable (D_Support × D_Expand). With concerns that passengers traveling during peak and off-peak hours may have different perception on bus performance, we also created variables representing morning (6:00 - 9:00) and evening (16:30 - 19:00) peak hours and tested them in the model; but, these variables were not statistically significant. 5.2 Marginal Effects The estimate marginal effects are provided in Table 5. The sum of these effects for each explanatory variable is zero (e.g., Speed: − 0.0001 − 0.0166 − 0.1188 + 0.1355 = 0). The interpretation is said, for instance, an increase in the operating speed by one categorical score is associated with being approximately 1.7% less likely to be in the “medium,” 11.9% less likely to be in the “good,” and 13.6% more likely to be in the “very good” bus performance categories. It was a surprise that the estimate coefficient of “D_Taxi” was not significant in the main model (Table 4) but it became significant when we computed its marginal effects (Table 5). The bus performance was approximately 6.1% more likely to be in the “good” category and 26.7% less likely to be in the “very good” category when it was evaluated by the passengers who previously commuted by a taxi. This might be resulted from the differences in the perceived service quality (e.g., comfort, cost, and convenience) provided by the public bus and taxi.

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Table 5 Marginal effects of the variables for the evaluation of bus performance Variable Very bad Personal characteristics Age Gender D_Student D_Staff D_Self-employed D_Housewife Previous transportation modes D_Own-car D_Motorcycle D_Motodop D_Remork D_Taxi Access and egress distance Access Egress Subjective evaluation on bus attributes Speed Fare Safety Comfort Free opinions D_Support D_Expand D_Support × D_Expand -

Marginal effects of ordered probit model Bad Medium Good Very good 0.0000 0.0000 0.0001 0.0002 0.0002 0.0005

0.0013 −0.0047 0.0135 0.0207* 0.0248 0.0428*

0.0093 −0.0106 −0.0329 0.0376 0.0816 −0.0952 0.1324** −0.1533** 0.1147** −0.1397** 0.1490** −0.1923**

0.0001 0.0002 0.0002 0.0000 0.0022

0.0099 0.0198** 0.0213 0.0021 0.1062

0.0578 −0.0678 0.1418** −0.1618** 0.1133** −0.1348** 0.0145 −0.0166 0.1607** −0.2691**

0.0000 0.0002 0.0018 0.0000 −0.0027** −0.0187** −0.0001 −0.0001 −0.0002 −0.0001

−0.0166** −0.0139** −0.0283** −0.0095**

−0.1188** −0.0994** −0.2028** −0.0680**

−0.0020 0.0214** 0.1355** 0.1134** 0.2313** 0.0776**

−0.0001 −0.0182** −0.1618** 0.1801** −0.0001 −0.0097** −0.0701** 0.0799** 0.0007 0.0528** 0.1743** −0.2278**

Variable descriptions are shown in Table 3 *p < 0.1, **p < 0.05

The absolute marginal effects increase with categorical score of bus performance; negligible effects can be said for the “bad” category, and slight effects for the “medium” category. Therefore, the overall bus performance being evaluated by passengers appeared to be in the “good” and “very good” categories. This might be the case since the newly operating public bus provided passengers safety, comfort, and relatively low fare. The bus itself is a symbol of city development, better environment (less air pollution), better traffic flow, reduction of private vehicles on the city streets, and possible decline in the road accident rate. The predicted probabilities of bus performance are 0.1%, 5.1%, 55.3%, and 39.5% (which are very close to the actual probabilities: 0.1%, 5.0%, 55.9%, and 39.0%, respectively) for “bad,” “medium,” “good,” and “very good” categories, respectively. This implies that the model perform reasonably well in predicting the bus performance perceived by its passengers.

6. DISCUSSION This study looked into details on what should be the significant factors influencing passengers’ perceived public bus performance in Phnom Penh. The variables of personal characteristics and previous travel modes were found to have negative effects on the perceived bus performance. Since the public bus service in Phnom Penh is still new, citizens seem not fully familiar with the service yet, comparing to their previous travel modes. However, the results

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revealed that the bus performance can be improved by enhancing the bus attributes and by addressing the concerns in the free opinions raised by passengers. Regardless of the travel distance, the current bus fare (0.37 US$) is quite affordable for general citizens. Since the bus fare is relatively less expensive than other means of transportation (i.e., paratransit and private vehicles), the fare itself appears to already encourage substantial number of passengers to use the public bus service. At the early stage of promoting the public bus service, the implemented bus fare is reasonable with acceptable level of comfort (e.g., air conditioned buses). Without the current fare discount (e.g., free for all students), the passenger demand is doubtful. With this respect, a better fare structure such as discount for frequent commuter and distance-based fare should be considered in order to prevent the revenue shortfall. An establishment of the government subsidy program is also required to sustain the public bus service in Phnom Penh when the bus operator faces financial matters. The bus operating speed needs to be improved. Since the public bus service was promptly introduced, there were inadequate infrastructures (e.g., proper bus stops with shelters and seats, sidewalk) and operational management (e.g., coordinated/priority traffic signalization, priority bus lanes) to ensure its smooth operation as well as to provide a more reliable public transport service. The average operating speed was measured to be approximately 10 km/h, which was fairly slow, leading to huge uncertainty of in-vehicle travel time and waiting time at bus stop. Further, the average evaluation score on the bus operating speed was 3.78 (Table 3), which was relatively lower compared to other bus attributes. When the bus operating speed is slow, the bus performance is likely to diminish whereas the passengers would consider an alternative mode with higher speed (e.g., motorcycle). The improvement on current bus operating speed is strongly linked to the current level of traffic congestion in Phnom Penh. The question is which strategies the government should consider and prioritize in order to improve the overall traffic flow in the city. In particular, Neth et al. (2005) pointed out that the efforts on road network improvements (e.g., paving road surface and connecting missing links) might not be sufficient to alleviate the traffic congestion, and the authors suggested for a more construction of new roads including ring roads. Construction of new roads appears to be a good idea, but this might not be the option since land prices and number of automobiles has considerably increased over the years. In addition to the transport infrastructure development, the government should consider more on transport-related policy such as vehicle growth control, big trucks in the city, on-street parking, and traffic regulation. The government should also consider other aspects to encourage commuters to use the public bus service, rather than basing on their private vehicles. This might be done by several ways. First, the bus service quality itself should be improved. Although bus performance was perceived to be high by bus passengers themselves, this is not necessarily true for general commuters. The public views on the image of the public bus are important. After the second bus experiment, the bus service was expanded with the fleet of 43 buses. The buses are second-hand and occasionally encounter several problems including fails of engine and airconditioning system. This has led to many complaints by bus passengers. Second, the accessibility to a bus stop (sidewalks) should be improved. Currently, it is unsafe for pedestrian to reach a bus stop as most of the available sidewalks are used for parking/vendors and no pedestrian bridge has been built yet. Fortunately, many bus stops of the three bus lines are now equipped with shelters and seats; so that, passengers could have themselves relaxed and protected from weather matters (hot and rain). Third, parking facilities such as park & ride and paratransit pick-up & drop-off points adjacent to a bus stop should be considered to encourage more intermodal commuters. This will provide commuters the convenience of

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seamless transfer among transport modes. Also, the advanced technologies such as Intelligent Transport System, information and communication technology, and electronic ticketing system should be considered since these technologies would substantially improve the level of bus service. A simple map of bus route network might also be very useful if it is attached inside the bus (not only at the bus stop), because many passengers are not familiar with the bus service and they don’t know which bus stop they shall get off for a transfer to other bus lines. Besides, the bus operator should enhance the internal regulations and provide adequate capacity building programs to staffs, because poor discipline among transit staffs can be a major cause of traffic congestion and accidents. Finally, the promotion of the public bus service through an effective media to the public might be helpful. Lon et al. (2013) acknowledged that the survey activities in their study made several residents aware of the necessity of the formal public transportation modes in Phnom Penh. Thus far, the enhancements on the above aspects and the bus attributes themselves would help to appeal for more bus passengers and would partially comply with the free opinions expressed by the bus passengers.

7. CONCLUSION We assessed the public bus performance in Phnom Penh through the passengers’ standpoints. The factors influencing on likelihood of passengers’ perceived bus performance were investigated under ordered probit model, using information collected from general citizens commuting by the public bus in the city during the service testing period. Results showed that factors significantly influencing on passengers’ perceived bus performance included individual occupations (i.e., staff, self-employed, and housewife), previous travel modes (i.e., own motorcycle and Motodop), bus attributes (i.e., speed, fare, safety, and comfort), and the individual opinions about the public bus service (i.e., support and expansion request). The availability of the public bus service is very important for citizens in Phnom Penh, because it provides safe and comfort trips with relatively low fare. The bus operating speed needs to be improved in order to provide a shorter waiting and travel time. The bus service helps reducing the traffic congestion level along bus routes, as some 33.1% of interviewed bus passengers reported they previously traveled by their own vehicles. With the public bus in operation, the air quality might gradually be improved and a number of lives could also be saved from road traffic accidents in the city. Based on the findings of this study, the public bus performance can be improved by the bus attributes and by addressing the concerns raised by passengers. The responsible authorities should therefore invest more on the improvements of transport-related infrastructures (e.g., sidewalk, bus stops, and park & ride facility), while effective policy/regulation (e.g., on-street parking, traffic control and management system) are also necessary to provide Phnom Penh’s citizens an efficient public transport system. By doing so, the public bus service might attract more passengers and the bus service itself could be sustained. To ensure the efficiency of the public bus operation, further research is required regarding the fare policy, bus capacity, and optimal service scheduling. The government should also consider other alternative modes of public transport system such as BRT and LRT, among others, in order to provide public with a more reliable public transport service. The sustainability of mass transit system (e.g., the public bus) in Phnom Penh depends not only on the public support, but also on the government strategies to sustain it. Phnom Penh is one of the fast-developing cities in Asia, so a careful consideration on urban transport planning is really important for the future of Cambodian people.

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ACKNOWLEDGMENTS The authors thank JICA study team, Mr. Masato KOTO, under the “Project Comprehensive Urban Transport Plan in the Phnom Penh Capital City (PPUTMP),” providing the data used in this study. The first author thanks Toshiba Corporation financial support. The contents of this paper reflect the viewpoints of the authors, who responsible for the remaining errors.

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