Transport sector is one of the largest CO2 emitters. As the economy dramatically grows, the travel demand is also significantly increased in developing countries. A huge number of vehicles traveling on the road have caused a large amount of CO2 emission into the air. Moreover, it causes heavy traffic congestions and traffic accidents that hinder the economic growth. Thus, it is necessary to integrate public transport into transport network. Public transport reduces CO2 emission, traffic congestion and also fatality by traffic accident. The most considerable public transport in developing countries is public bus, which is suitable for booting up public transport and raising public awareness of advantages of public transport. Responding to the issues above, Phnom Penh, capital city of Cambodia has put public bus in service in 2014 to reduce traffic congestion as well as car ownership. In order to promote bus ridership, there are many measures needed to improve bus operation, especially travel speed. A study report by JICA (2014) proves that the traffic congestion is a significant issue that causes the travel speed of vehicles declined. As public bus in Phnom Penh is operated in mixed traffic, the bus travel speed is also affected by congestion.

Bus rapid transit (BRT) is regarded as an emerging attractive alternative to improve the bus operation and travel speed at a low-to-moderate cost [1-4]. However, until recently heavy investments have been made exclusively in building metro and light rail systems in Asia [4]. Levinson 2003 reviewed 26 case study cities of BRT in United States and Canada, Australia, Europe, and South America, but not including Asia. Hensher and Golob 2008 assessed 44 BRT systems in operation throughout the world, and only one, TransJakarta at Jakarta, Indonesia in Southeast Asia is included although systems in China, Japan, South Korea and Taiwan are included. Maeso-Gonzalez and Pérez-Ceron 2014 concluded that there are substantial differences between the systems at developed countries in North America, Oceania and Europe and those at developing countries in South America, Asia and Africa. The former has the technologically advanced systems while the latter gets more consolidated among the population and more developed in terms of service.

However, the systems in Asia are not comparable in size or performance to those of South America, and some systems also lack innovation and are limited to unsuccessfully adapting the operations not suitable for the local needs [4]. Although Hensher and Golob 2008 stated the cost of providing high capacity integrated system is an attractive option in many contexts, BRT scheme consists of many features and requires a large amount of budget for developing compared to ordinary bus scheme from the view point of developing countries. Thus, as Nikitas 2015 stated, it is difficult to transform a concept that is often misunderstood into new local applications even if they could genuinely improve road traffic conditions, especially at developing countries in South Asia.

From the view point of low implementation cost, there are some features of BRT system that are worth to consider for better speed improvement. They are:

Dedicated right-of-way: Bus speed is increased if bus runs in a dedicated lane. However, other possible combination of vehicles running in bus lane should also be considered for optimizing both bus and other vehicles’ speed.

Bus stop distance: At each bus stop, bus decelerates and accelerates causing delay thus it is crucial to optimize distance of bus stops to improve bus speed.

Express, limited and local services: Bus speed is increased by providing limited service bus that stops only at high demand station and leaves low demand station to local service bus.

This study aims to find appropriate measures to improve bus speed with minimum budget at Phnom Penh, Cambodia. In order to minimize cost for improving bus speed, some policies such as lane management, optimizing bus stop distance and limited bus service are considered in this study.

Kuukka-Ruotsalainen found that travel speed and punctuality are improved by bus lane by 15% to 20% and delays caused by traffic lights are reduced by transit signal priority (TSP) by 40% to 50% [5]. Two-thirds of BRT speed increases in Los Angeles were result of less stops, whereas one-thirds were of TSP [6]. Distance from bus stop to intersection affects bus travel speed. Wang concludes that far-side stop (bus stop located after-crossing intersection) is better than near-side stop (bus stop located close to before-crossing intersection) with TSP application [7]. The study was made at signal controlled intersection with/without TSP application. Bus performance can be improved by minimizing bus stops [8]. Their study claims that travel time is reduced from 3.75 to 2.69 min/km by reducing bus stops from 5 to 3.75 per kilometer and dwell time from 20 to 15 seconds. However, these studies targeted on BRT only, and the effects on other vehicles were not investigated well. In order to fully investigate the effect of BRT on other vehicles, traffic simulation models are very useful. Lan and Chang and Meng developed cellular automata (CA) models of mixed traffic flow of car and motorcycle, and the former is applied to a highway in Taiwan [9,10]. CA model is also developed by Hu for mixed traffic flow of car and electric bicycle, and applied to an arterial in China [11]. Arasan and Koshy developed a microscopic traffic simulator, HETEROSIM, and applied to the mixed traffic flow of multiple types of vehicles including bus, truck, car, motorcycle and bicycle at an arterial in India [12]. Mu and Yamamoto used a simulation package, VISSIM, to examine the mixed traffic flow of car and microcar at an urban arterial network in Japan [13]. BRT was not on the focus of the simulation analyses mentioned above, but they suggest the traffic simulation models are suitable for investigation of the mixed traffic flow. In our study, VISSIM is used as a simulation tool as it is one of the most useful and reliable simulators [14].

On the effect of BRT, Tranhuu used a simulator, SATURN, to investigate the effect of motorcycle on bus lane in Vietnam [15]. They applied the model within four distinct situations caused by the motorcycle. They are very strong level, medium level, weak level, and no violation of motorcycles in bus lane. Their research concluded that strong enforcement in general traffic can reduce the bus travel time. Arasan and Vedagiri applied HETEROSIM to investigate the effect of bus stop and exclusive bus lane in India [16]. They obtained threshold values of traffic flow for provision of exclusive bus lanes under different roadway and traffic conditions. Wei applied CA model to simulate the movement of car and bus, and evaluated the effect of TSP on vehicles’ waiting time at intersection, thus fuel consumption and exhaust emission [17].

Khan and Maini stated in their review that studies of traffic flow for non-lane- based mixed traffic conditions in developing countries are limited, and that the flow characteristics of mixed traffic is dependent on roadway geometry, traffic conditions, and static and dynamic properties of vehicles in the traffic stream. Thus, the properties of motorcycle as well as other vehicles should be well represented in the simulator for our purpose since the motorcycle is dominant in Phnom Penh, Cambodia. Several studies have been carried out to investigate the properties of motorcycle in the mixed traffic on many countries including Kenya, UK, Vietnam, Indonesia and India, but not in Cambodia [19-24].

One way to make the simulator adjust to the local context is the calibration of the parameters in the simulator, and the calibration and the validation are a critical step although sometimes informally practiced [25]. Park and Schneeberger 2003 applied a Latin hypercube sampling (LHS) design to sample an orthogonal array of parameters randomly from the entire design space, and estimated a regression model for the surface function of the target variable such as travel time with the outputs of the simulations with LHS parameters [26]. Then, the candidate parameters were evaluated for validation. LHS was also used by Park and Qi in combined with genetic algorithm (GA) for optimizing the parameters [27]. GA was used by Mathew and Radhakrishnan and Manjunatha too [28, 29]. However, the focus of the studies was a signalized intersection, and their target variable was univariate.

In our study, VISSIM simulator is applied to a road segment containing several intersections without signal control in Phnom Penh. Since the focus of our study is not only bus speed but also the effects on other vehicles, we need to validate simulated travel times of other vehicles including car and motorcycles in addition to bus. Conventional GA for univariate target is not suitable while GA for multivariate target may take more computation time than practically acceptable. Thus, we develop a structural equation model to obtain the multivariate surface function after simulation runs with LHS parameters. Then, the candidate parameter sets are evaluated for validation. Proposing the calibration process for multivariate target is one of our contributions.

Empirically, our contribution is to identify appropriate measures to improve bus speed at a low-to-moderate cost in Phnom Penh, Cambodia. Phnom Penh is in motorcycle dominant mixed traffic condition, which is typical for several South Asia as well as India. Thus, the findings of the current study can offer fundamental insights to these megacities.