Yue Su
email: yuesu@my.swjtu.edu.cn

Ph.D Xiaobo Liu
email: xiaobo.liu@swjtu.cn

Guo Lu

Ph.D Wenbo Fan
email: wbfan@swjtu.edu.cn

Optimal Design of Bus Routes for Different Vehicle Types

Keywords: Public Transportation Planning and Development AP025, Model, Transit, Transit Management and Performance AP010, Planning, Bus Route Design, Pollution, Driving Regimes, Continuum Model, Different Vehicle Types

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INTRODUCTION

With the astounding growth in automobile ownership, most cities in China have to face a number of transportation related issues such as rapid environmental deterioration and serious congestion problem in urban area. The pollutants emitted such as CO, VOC, and NO x , bring unpleasant impacts on the air quality as well as the public health. Zero-emission buses, such as battery electric buses and supercharge buses, entail environmental friendliness, and have been recognized as a new solution of environmental problem and traffic congestion. Supercharge bus is the newest bus type in the market and it utilizes super capacitor as power source, which has the advantages of fast charging and discharging ability. Compared with battery electric bus, supercharge bus takes far less time to charge, and it doesn't need to replace batteries in a life cycle. Many cities in China have spared no effort in developing a new transit network with clean-energy buses. For instance, all the conventional buses (i.e., [START_REF] Medina | Model for the Optimal Location of Bus Stops and Its Application to a Public Transport Corridor in Santiago, Chile[END_REF],000 CNG and diesel buses) in Shenzhen will be replaced with electric buses by the end of 2018 (1). Despite the environmental advantages of the new-energy buses, the expensive purchase price is an obstacle that impedes the progress of the shifting to clean energy. For example, the unit vehicle price of an electrical bus is almost three times more expensive than conventional bus (2). In order to yield a scientific assessment between clean-energy buses and conventional buses, it is necessary to take the technical and economical characteristics of different bus types into consideration to assess their cost competiveness.

To make the transit system more efficient, it is necessary for designers to provide a delicated transit system with stops being well located. There are two basic approaches for tackling Transit Route Design (TRD) problem. One is discrete approach which has been frequently used in the TRD problem. Studies (3-6) applied discrete approach to decide the optimal stop locations. The basic idea of discrete model is to determine optimal set of stops for a given OD matrix and predict travel time per link on the route (7). Dynamic programming is largely used in the optimization. The disadvantage of this approach is the computational inefficiency because of the numerous decision variables.

Another approach is the Continuum Approximation (CA) approach. Previous studies (8-18) employed CA approach to obtain the optimal design of a transit system, where a small number of continuous functions are inputed; e.g., lines and stations are specified in terms of the spacing between them [START_REF] Daganzo | Logistic Systems Analysis[END_REF]. Some other parameters including headway, bus capacity, fleet size, have also been used in the optimization model [START_REF] Byrne | Cost Minimizing Positions, Lengths and Headways for Parallel Public Transit Lines Having Different Speeds[END_REF][START_REF] Sivakumaran | Cost-savings Properties of Schedule Coordination in a Simple Trunk-and-feeder Transit System[END_REF]. The pioneering work of CA approach employed in transit route design seems to be the study of Newell (8), in which the mechanics of CA approach was elaborated. After that, Vaughan and Cousins [START_REF] Vaughan | Optimum Location of Stops On a Bus Route[END_REF] took advantage of a continuous stop density function to determine the number of stops so as to minimize user's travel time on a single corridor. The demand pattern is a "many-to-many" demand pattern to reflect the spatial heterogeneity of demand and is also presented in a continuous form. Later, Wirashinghe and Ghoneim (12) proposed a more general continuous model to minimize the total system cost which consists of user cost and operator cost with respect to a "many-to-many" demand pattern. The stop density is expressed as a function of location and the integral of stop density function is firstly used as the method to find optimal stop locations. Recently, Medina-Tapia et al. [START_REF] Medina | Model for the Optimal Location of Bus Stops and Its Application to a Public Transport Corridor in Santiago, Chile[END_REF] employed CA-based transit design model to a single bus corridor considering multiple periods (peak hour and off-peak hour). A bi-directional stop density functions and multi-period headways are obtained. Further, Amirgholy et al. [START_REF] Amirgholy | Optimal Design of Sustainable Transit Systems in Congested Urban Networks: A Macroscopic Approach[END_REF] proposed a CA model to minimize user cost, agency cost, and pollutant cost in a congested network. It seems to be the only CA-based work that considering environmental impacts into transit route design.

The theme of this study is to optimize the bus route design for a single corridor considering different bus types of which the inter-stop driving regimes are explicitly modeled. In the proposed model, the objective function is to minimize the total system cost, as a sum of user cost, operator cost, as well as pollutant cost. The model is based on continuum approximation and its accuracy is verified by a discrete approach. A case study is furnished on a bus route in Yaan City (China). The contributions of this work include: the environmental factors and inter-stop driving regimes are considered in transit route design model to analyze the system cost of different vehicle types. By intergrating the optimized system cost (on a per day basis) with life-cycle cost analysis, it can provide operator insight to choose appropriate vehicle type. Furthermore, the optimized coasting speeds for different bus types have been analyzed as well as the effect of coasting regimes on operation cost, which can be implemented in practical selection of driving regimes for different bus types. The remainder of this paper is organized as follows. Next section introduces the optimization model, with variables' definitions and formulations of the CA model, respectively. In the section, a non-linear total cost minimization problem is introduced. After that, the numerical application is conducted by using the real demand data, and the obtained results are analyzed. In the end of section 3, a cost-effectiveness analysis among different vehicle types has been elaborated. Last section concludes the findings and indicates further extensions.

METHODOLOGY

The proposed model contains a bus corridor of length L, operating in two directions (denoted by 𝑟 ∈ {𝑒𝑏, 𝑤𝑏}, indicating eastbound and westbound, respectively). The bus travelling in each direction of the corridor stops at each stop. The studied periods are: peak hour period and off-peak hour period (denoted by 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, indicating peak-hour period and off-peak hour period, respectively) whose service headways are different to reflect the demand variation but equal in both directions. To facilitate organization, the developed model is based on a few assumptions as follows:

1. The value of time for all passengers is assumed to be the same, regardless the citizen's status, income, etc.

2. Passengers choose the nearest stop to board or alight bus.

3. There is no congestion on the corridor, so bus acceleration or deceleration results from whether it has to stop or start at bus stop. For the segment between two stops, different driving regimes could be adopted regarding whether coasting occurs, for instance. In addition, the acceleration and deceleration rates are constant in this study.

4. No bus congestion is assumed to occur at bus stops, so each bus opens its door as soon as it arrives at a stop.

In this section, we will first introduce the objective function 𝑇𝐶 𝑚 , which represents the total system cost (on a per day basis), with 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, representing supercharge, CNG, Lithium-ion, and diesel buses, respectively. 𝑇𝐶 𝑚 contains user cost 𝐶 𝑢,𝑚 , operator cost 𝐶 𝑜,𝑚 , and pollutant cost 𝐶 𝑃,𝑚 :

𝑇𝐶 𝑚 = 𝐶
𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖} The expression of pollutant cost 𝐶 𝑃,𝑚 will be formulated later in Equation 30 and 31.

Continuum Approximation Models User Cost

Seeing in Equation 1a, each cost item will be explained in brief expression in this part.

For users who access and egress at point x on the corridor, the access/egress cost is computed by multiplying the number of users, the value of access time, and the average walking time. Assuming that the distribution of demand in the neighborhood of x is uniformed, the expected walking distance can be formulated as

1 4•𝛿 𝑚,𝑟 (𝑥)•𝑣 𝑎
, thus the access/egress cost is: The daily waiting cost is considered as the product of average waiting time per passenger, the number of boarding passengers, and the value of waiting time. Considering that waiting passengers will get on the first bus that passes by, the waiting time of passengers will increases linearly with average headway between buses, ℎ 𝑖 . The expected passenger's waiting time will be half of headway if bus's arrival is perfectly regular. Therefore, the expression for user waiting cost is given as follow: The in-vehicle cost is the cost generated by all on board passengers when bus is operating between stops. It contains the cost accounted for bus travelling and for bus idling. Different driving regimes might be conducted while bus is moving to next stop and it is essential to fully understand the driving scenarios that could happen. Generally, there are four basic driving regimes (i.e., acceleration, cruising, coasting, and deceleration). The bus's travel time between stops depends on whether a transit vehicle can reach its maximum speed or not. Supposed 𝑆 𝑐 to be the critical distance for completing a perfect acceleration to cruising speed and a perfect deceleration from the cruising speed. For a stop spacing (represented as 1 𝛿 𝑚,𝑟 (𝑥) ), here are four driving scenarios: Scenario 1: No coasting and cruising, 1 𝛿 𝑚,𝑟 (𝑥) ≤ 𝑆 𝑐 In this case, the inter-stop travel time 𝑡 1 consist of two parts, the time for accelerating to some speed 𝑣 𝑠 (𝑣 𝑠 ≤ 𝑣 𝑐,𝑖 , 𝑣 𝑐,𝑖 is the cruising speed, with 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}) and time for braking; The time items are expressed as follows: In this scenario, there are three intervals in an inter-stop traveling: the time for accelerating to cruising speed 𝑣 𝑐,𝑖 (𝑡 𝑎2 ); the time for cruising (𝑡 𝑐2 ); and the time for braking(𝑡 𝑏2 ). The travel time for each regime and the total travel time in a stop spacing for this scenario (𝑡 2 ) are: Similarly, there are three intervals composed in this scenario: accelerating, coasting to speed 𝑣 𝑐𝑜 with deceleration rate 𝑐 𝑣 (km/h 2 ), and then braking regime is applied to reach the stop. Thus, we can determine the inter-stop travel time for each regime as follows:

𝐶 𝑎,𝑚 = ∑ ∑ ∫ (𝑎𝑙 𝑟,𝑖 (𝑥)+𝑏𝑜 𝑟,𝑖 (𝑥))•𝑇 𝑖 •𝜃 𝑎
𝐶 𝑤,𝑚 = ∑ ∑ ∫ 𝑏𝑜 𝑟,𝑖 (𝑥) 𝐿 0 • 𝑇 𝑖 • ℎ 𝑖 2 𝑖=𝑝𝑘,𝑜𝑝 𝑟=𝑒𝑏,𝑤𝑏 • 𝜃 𝑤 𝑑𝑥, (3)
𝑡 1 = 𝑡 𝑎1 + 𝑡 𝑏1 (
𝑡 2 = 𝑡 𝑎2 + 𝑡 𝑐2 + 𝑡 𝑏2 (
𝑡 3 = 𝑡 𝑎3 + 𝑡 𝑐𝑜3 + 𝑡 𝑏3 (9) 𝑡 𝑎3 = 𝑣 𝑐,𝑖 𝑎 𝑣 (9a) 𝑡 𝑐𝑜3 = 𝑣 𝑐,𝑖 -𝑣 𝑐𝑜 𝑐 𝑣 (9b) 𝑡 𝑏3 = 𝑣 𝑐𝑜 𝑑 𝑣 (9c) with 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}; 𝑡 𝑚,𝑟,𝑖,3 𝑣 (𝑥) = (𝑣 𝑐,𝑖 • ( 1 𝑎 𝑣 + 1 𝑐 𝑣 ) + 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) • ( 1 𝑑 𝑣 - 1 𝑐 𝑣 )) • 𝛿 𝑚,𝑟 (𝑥), 1 𝛿 𝑚,𝑟 (𝑥) ≥ 𝑆 𝑐 , (10)
𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} ; With 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) (indicating the speed at the end of coasting with 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏}, 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, at point x) expressed as below, which is derived from the work of Vuchic [START_REF] Vuchic | Urban Transit Systems and Technology[END_REF]:

𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) = √ 2𝑎 𝑣 •𝑑 𝑣 •𝑐 𝑣 -(𝑎 𝑣 +𝑐 𝑣 )•𝑣 𝑐,𝑖 2 •𝑑 𝑣 •𝛿 𝑚,𝑟 (𝑥) 𝑎 𝑣 •(𝑑 𝑣 -𝑐 𝑣 )•𝛿 𝑚,𝑟 (𝑥) , (11)
𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} ; Scenario 4: With coasting and cruising,

1 𝛿 𝑚,𝑟 (𝑥) ≥ 𝑆 𝑐
In this scenario, buses can accelerate to cruising speed, then keep constant speed, coast from cruising speed, and brake. Thus, four intervals are included in the inter-stop travel time.

𝑡 4 = 𝑡 𝑎4 + 𝑡 𝑐4 + 𝑡 𝑐𝑜4 + 𝑡 𝑏4 (12)
The distance for an inter-stop cruising is:

𝑆 𝑚 = 1 𝛿 𝑚,𝑟 (𝑥) -( 𝑣 𝑐,𝑖 2 2𝑎 𝑣 + 𝑣 𝑐,𝑖 2 -𝑣 𝑐𝑜 2 2𝑐 𝑣 + 𝑣 𝑐𝑜 2 2𝑑 𝑣 ), (13)
𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} ;

We denote the inter-stop cruising time as 𝑡 𝑐4 , and present it in the following equation:

𝑡 𝑐4 = 1 𝛿 𝑚,𝑟 (𝑥)•𝑣 𝑐,𝑖 - 𝑣 𝑐,𝑖 2 ( 1 𝑎 𝑣 + 1 𝑐 𝑣 ) - 𝑣 𝑐𝑜 2 2𝑣 𝑐,𝑖 ( 1 𝑑 𝑣 - 1 𝑐 𝑣 ), (14)
𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} ; Thus, the inter-stop travel time is:

𝑡 4 = 1 𝛿 𝑚,𝑟 (𝑥)•𝑣 𝑐,𝑖 + ( 1 𝑎 𝑣 + 1 𝑐 𝑣 ) • 𝑣 𝑐,𝑖 2 + ( 1 𝑐 𝑣 - 1 𝑑 𝑣 ) • ( 𝑣 𝑐𝑜 2 2𝑣 𝑐,𝑖 -𝑣 𝑐𝑜 ), (15)
with 𝑖 ∈ {𝑝𝑘, 𝑜𝑝};

It should be mentioned that, even though it exists four different operation scenarios, the scenario 1 to 3 can be regarded as the special situations of scenario 4 (explained later), which the per-kilometer travel time 𝑡 𝑚,𝑟,𝑖 𝑣 (𝑥) and the time covered by each driving regime in per-kilometer distance are: (

𝑡 𝑚,𝑟,𝑖 𝑣 (𝑥) = 𝑡 𝑎,𝑚,𝑟,𝑖 𝑣 (𝑥) + 𝑡 𝑐,𝑚,𝑟,𝑖 𝑣 (𝑥) + 𝑡 𝑐𝑜,𝑚,𝑟,𝑖 𝑣 (𝑥) + 𝑡 𝑏,𝑚,𝑟,𝑖 𝑣 (𝑥) (16)
1 𝑑 𝑣 - 1 𝑐 𝑣 )] • 𝛿 𝑚,𝑟 (𝑥) (16d) 𝑡 𝑚,𝑟,𝑖 𝑣 (𝑥) = [( 1 𝑐 𝑣 - 1 𝑑 𝑣 ) • 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 2 (𝑥) 2𝑣 𝑐,𝑖 -( 1 𝑐 𝑣 - 1 𝑑 𝑣 ) • 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) + 1 𝑣 𝑐,𝑖 •𝛿 𝑚,𝑟 (𝑥) + 𝑣 𝑐,𝑖 2𝑎 𝑣 + 𝑣 𝑐,𝑖 2𝑐 𝑣 ] • 𝛿 𝑚,𝑟 (𝑥), (17)
𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} ; In scenario 2, which doesn't contain coasting regime, it means that the coasting speed equals to the cruising speed, 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) = 𝑣 𝑐,𝑖 . Thus, Equation 17 can be converted into Equation 8. As for scenario 3, which has no cruising regime, the cruising time is zero (𝑡 𝑐,𝑚,𝑟,𝑖 𝑣 (𝑥) = 0), and we can find the expression of 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) the same as Equation 11. Scenario 1 is the combination of two conditions (𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) = 𝑣 𝑐𝑖 and 𝑡 𝑐,𝑚,𝑟,𝑖 𝑣 (𝑥) = 0) and the Equation 6 can be yielded. Therefore, Equation 17is the generalized formula with four scenarios included. We can make an assumption that scenario 4 is conducted in every inter-stop trip. By plugging𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) into optimization, we can finally yield the profile of 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥). Once we get the 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) profile, the effect of coasting regime on operation cost can be explored.

After bus finishes its inter-stop travel, the bus dwell time will generate for passengers boarding and alighting and for opening and closing doors. Supposing that an onboard fare payment method is applied and one door is used for boarding and one or more doors are used for alighting, the dwell time at each stop is dominated by the process that takes longer. To facilate modeling, we convert dwell time at each stop to dwell time on per kilometer basis, denoted as 𝑡 𝑚,𝑟,𝑖 𝑑 (𝑥). The expression of 𝑡 𝑚,𝑟,𝑖 𝑑 (𝑥) is:

𝑡 𝑚,𝑟,𝑖 𝑑 (𝑥) = 𝑡 𝑑 (𝑥) • 𝛿 𝑚,𝑟 (𝑥) (18) Where 𝑡 𝑑 (𝑥) = 𝑡 0 + 𝑚𝑎𝑥 (𝑏𝑜 𝑟,𝑖 (𝑥) • ℎ 𝑖 • 1 𝛿 𝑚,𝑟 (𝑥) • 𝑡 𝑏 , 𝑎𝑙 𝑟,𝑖 (𝑥) • ℎ 𝑖 • 1 𝛿 𝑚,𝑟 (𝑥) • 𝑡 𝑎 ), (19)
𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} ; 𝑡 0 is the dead time for opening and closing doors, 𝑡 𝑏 and 𝑡 𝑎 are the average boarding and alighting time (hour) per passenger, respectively.

To summarize, the expression for in-vehicle cost is presented as follow: Operator Cost Each cost item in Equation 1b will be discussed in this part. The cost for stop construction and maintenance is determined by 𝜃 𝑓 and 𝜃 0 , which represent the daily cost for stop construction ($/day) and the hourly cost for stop maintenance ($/hour), respectively. Thus, the per-day cost for stops on the corridor is:

𝐶 𝑣,𝑚 = ∑ ∑ ∫ 𝑃 𝑟,𝑖 (𝑥)
𝐶 𝑠,𝑚 = ∑ ∫ (𝜃 𝑓 + 𝜃 0 𝑇) 𝐿 0 • 𝛿 𝑚,𝑟 (𝑥)𝑑𝑥 𝑟=𝑒𝑏,𝑤𝑏 , (21)
𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} The first constraint indicates that the bus's capacity must feed the total passenger demand (𝑖 ∈ {𝑝𝑘, 𝑜𝑝}). The second constraint is the stop capacity should satisfy the total demand of boarding and alighting. In addition, the optimal results of headway, stop density, and coasting speed should be positive.

The objective function has three variables/functions: 𝛿 𝑚,𝑟 (𝑥), ℎ 𝑖 , 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥), and the constrains are nonlinear. Those factors increase the complexity of the optimization. To obtain the analytical expression of each variable/function, two alternative procedures are proposed. Firstly, we introduced an initial value of headway to reduce the dimension. We can obtain the expression of coasting speed and stop density by solving first order condition 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) * = 𝑓(𝑥, ℎ 𝑖 ) and 𝛿 𝑚,𝑟 * (𝑥) = 𝑓(𝑥, ℎ 𝑖 , 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) * ). Through this approach, the model is transformed into a problem that has ℎ 𝑖 variables.

The second procedure contains two steps: in the first place, 𝛿 𝑚,𝑟 (𝑥) and 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) are replaced by the optimal expression 𝛿 𝑚,𝑟 * (𝑥) and 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) * . As the optimal function of headway also contains stop density and coasting speed, which is ℎ 𝑖 * = 𝑓(𝑥, 𝛿 𝑚,𝑟 (𝑥) * , 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) * ), the next step is to iterate the analytical expression of headway, stop density, and coasting speed until the convergence is reached under constraints.

The first-order-condition expressions of stop density in each direction is:

𝛿 𝑚,𝑟 (𝑥) = √ ∑ (

;

Considering the constraint (0 ≤ 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) ≤ 𝑣 𝑐,𝑖 ), the optimal expression of 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) is given by: 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) = mid(0, 𝑣 ̃𝑐𝑜,𝑚,𝑟,𝑖 (𝑥), 𝑣 𝑐,𝑖 ),

𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏}, 𝑛 ∈ {1,2,3} ; where function mid(𝑥, 𝑦, 𝑧) return the middle value among 𝑥, 𝑦, and 𝑧.

As for supercharge bus and Lithium-ion bus, the pollutant emitted can be neglected, so the optimal expressions of stop density, headway, and coasting speed are simplified as follows: which two of them are clean-energy bus (i.e., supercharge bus and Lithium-ion bus), the others are conventional buses (i.e., CNG buses and diesel buses). It should be mentioned that all the vehicles are 12m in length. The emission standard for CNG bus and Diesel bus is both of China National IV standard. The emission rates of conventional buses at different driving regimes are given in Table 2, the data is adopted from previous studies [START_REF] Qu | Improving Vehicle Emission of Bus Transit with Integrated Service and Different Vehicle Size[END_REF][START_REF] Qian | Urban Diesel Bus Emission and Its Application[END_REF] and is summarized below. 0.0070 0.0212 0.0069 0.0137 The boarding and alighting density function is obtained by using the on and off data which is collected on 6 th April, 2017 by Shu Tong Transportation Agency. All the programming process is performed on Matlab platform. Figure 2 shows the boarding and alighting densities along the corridor in terms of different time periods (i.e.

𝛿 𝑚,𝑟 (𝑥) = √ ∑ (

, peak hour and off-peak hour). FIGURE 2 Boarding and alighting densities along the corridor in different time periods

The values of walking time, riding time, and waiting time are derived from [START_REF] Medina | Model for the Optimal Location of Bus Stops and Its Application to a Public Transport Corridor in Santiago, Chile[END_REF], and are set at 4.09$/hour, 1.64$/hour, 2.73$/hour, respectively. Passengers access/egress bus stop at 3.6km/h. The time for opening and closing doors is 2s at each stop. According to the schedule provided by Shu Tong Transportation Agency, in the day time, from 7:00 to 17:00, the headway in current system is 7 min; and during the evening peak, which is 17:00-19:00, the service headway is 12min; for 19:00-20:00, the service headway is 15 min; 20:00-21:00, the service headway prolonged to 20min.

Optimal Design Solution Analysis

Figure 3 presents the discretization of the bi-directional stop density functions for four transit modes. We discretize the bi-directional stop density functions by locating stops when the integral of its left boundary and right boundary is 1. Here, we take the center line of each stop spacing as the left/right boundary. The detailed description of the discretization method is derived from ( 16). In the Figure, the circles on the stop density curves represent the optimal location of stops and the dash lines represent the boundaries of each stop coverage market. The system characteristics of current and optimized 7 th route in Yaan (City) are summarized in Table 3. In Table 3, we compare the optimal system costs of four vehicle types. The optimal results 4 indicate that when CNG buses are operating on the route, the system cost is the lowest. Note that 5 the cost gap between CNG buses system and supercharge buses system is slight (8080.9$/day vs. 6 8127.4$/day). Then, we discretize the optimal results based on CA approach and we obtain the real 7 results when CA models are applied practically, which are shown in the third row. Comparing with 8 the optimal results with discretized results, the outcomes of discrete models are in neighborhood 9 with that of CA models, with error less than 3%. In other words, if we employ the discretized 10 results on a real bus route, the system cost will be closed to optimal value, which verified the 11 applicability and accuracy of CA models. Finally, we compare the current system costs (detailed in 12 user cost, operator cost, and pollutant cost for conventional buses), with optimal results and 13 discretized results. Remarkably, the optimal results have saved more than 50% of the system cost, 14 especially in user cost. 15 In terms of system design, comparing with the current route configuration, the average stop spacing is increased by up to 31.3%. As for the service headway, the optimal headway in peak hour is ranging from 6.55 min to 8.30 min with respect to vehicle type. Among them, CNG is the most frequently emited bus type. According to optimal design, the optimal headway in peak hour has been decreased by up to 31%, while in off-peak hour, the optimal headways are approximately equal to the observed values in current system. From the two figures above, the optimal coasting speed profiles of four different vehicle types in the same period have the similar trend, but with distinguishing differences in average values. By discretizating the coasting speed functions, we can obtain the average values of coasting speeds in peak/off-peak hour on two directions. The results are summarized in Table 4, from which supercharge bus has the highest average coasting speed regardless of the time period. By contrast, diesel bus has the lowest average coasting speed. The results are reasonable because the diesel bus has the highest per-kilometer energy cost. In order to conserve energy, it's more beneficial to coast, as coasting regime will have less energy consumption than cruising regime. As for supercharge bus, whose per-kilometer energy cost is slight, it's more profitable to drive at cuising speed in order to save travel time. .50 Driving regimes have direct impact on operation cost. In light of this, a system-cost comparison is conducted between optimized systems that introducing coasting regime and the one without considering coasting regime. The results are concluded in Table 5 and the operation costs in no-coasting system increase by up to 7%, while user costs decrease more than 12.9%, mainly in access/egress cost. The reason is that, introducing coasting regime will reduce the number of stops along corridor, leading to an increase in access/egress cost. However, the in-vehicle costs for two models are in neighborhood (with difference less than 5%), which means the travel time is closed for two systems, even when coasting regime is added. As a result, the distance traveled under cruising regime (with higher per-kilometer cost) will decrease. That's the reason why operation cost will decrease when introducing coasting regime in the system. Thus, coasting regime has a positive impact in view of operator, but passengers should accept a higher user cost. To summarize, for clean-energy buses (i.e., supercharge buses and Lithium-ion buses), it's more profitable to drive at cruising speed, while for conventional buses (i.e., CNG buses and diesel buses), introducing coasting regime is justifiable, considering the cost saving in operation and energy. 3, we can find the most economical vehicle type is CNG buses. Historical data shows that the maintenance cost for CNG and diesel bus will increase by year, even though their initial capital cost is lower. As a result, the cumulative costs of conventional buses in a life span will also be expensive. According to the maintenance cost data that Shu Tong Transportation Agency provided, clean-energy buses have a constant maintenance cost but a high initial capital cost. Thus, there exists a trade-off: which type is the most economical in a life span (8 years). Inspaired by the previous studies (2, 26), which explored the varying maintenance cost by years in terms of diesel buses and CNG buses by using monthly maintenance data, we can roughly analyze the life cycle cost of our buses. The relationships of maintenance cost changing by year for diesel bus and CNG bus are shown respectively: 𝑦 = 0.025 + 0.029 * 𝑧 , which y is presented the cost of maintenance per kilometer ($/km) for diesel bus, z is the bus age (years). 𝑦 = 0.02 + 0.017 * 𝑧, which y is the maintenance cost per kilometer for CNG bus ($/km), z is the bus age (years). We investigated a cost comparison for four bus types as shown in Figure 6. The results present that without including the pollutant cost into calculation, the cumulative costs of CNG bus will surpass those of supercharge bus after 13 th year. On the contrary, when pollutant costs for conventional buses (i.e., CNG buses and diesel buses) are taken into consideration, supercharge bus will outperform other bus types after 8 years, which is still out of a life span. Thus, clean-energy buses are less competive than conventional buses unless the government provides subsidies.

(a) Life-cycle analysis without pollutant costs (b) Life-cycle analysis with pollutant costs

FIGURE 6 Life-span cost comparison CONCLUSION AND FUTURE EXTENSION

In this research, we developed a muti-period continuum approximation to optimize total cost including user cost, operator cost, and pollutant cost, where environmental impact and various driving regimes are explicitly considered. The proposed model is applied to four different vehicle types to optimize stop locations and service headway. The optimization of a real-word bus route results in significant reduction of total cost (over 50%). Comparing with current transit design, the average stop spacing is increased by 31.3%, and the peak-hour headway is decreased by up to 31%.

The accuracy of proposed model is verified by a discrete model with error less than 3%. The huge gap of total system cost between current design and optimized design indicates that current design might not well fit the demand, adjustment should be done in terms of stop locations and headway.

To investigate the effect of coasting regime, the optimal coasting profiles and the average values on two directions in multiple periods is compared in terms of different bus types. The results indicate that clean-energy buses have a shorter coasting distance while conventional buses need to coast longer in order to conserve energy. The analysis of the effect of coasting regime on operation cost suggest that introducing coasting regime will reduce operation cost (by up to 7%) but increase user cost (by up to 13%).

In the life-span cost effectiveness comparison amongst four different vehicle types, the economic feasibility of supercharge bus has been discussed. On a life-span scale (normally 8 years), the total cost of supercharge will be the lowest among four vehicle type after 8 th years, when pollutant costs are considered. It provides an insight that from the prespective of operator, shifting conventional buses to new energy buses is not profitable unless subsidies are provided by government.

For future extension, it is interesting to compare the stop-skip service with current all-stop design. Additionally, the effect of coasting for different transit modes, such as rail and Bus Rapid Transit (BRT) system can be further studied.

  Where 𝑡 𝑎,𝑚,𝑟,𝑖 𝑣 (𝑥) = 𝑣 𝑐,𝑖 •𝛿 𝑚,𝑟 (𝑥) 𝑚,𝑟,𝑖 (𝑥)•𝛿 𝑚,𝑟 (𝑥) 𝑑 𝑣 (16c) 𝑡 𝑐,𝑚,𝑟,𝑖 𝑣 (𝑥) = [ 1 𝛿 𝑚,𝑟 (𝑥)•𝑣 𝑐,𝑖 -

FIGURE 3

 3 Supercharge bus stop locations (b) CNG bus stop loactions (c) Li-thium-ion bus stop locations (d) Diesel bus stop locations 1 Optimal stop locations 2 3

  The operator cost entails those of: the stop construction and maintenance cost 𝐶 𝑠,𝑚 ; the Vehicle Hour Traveled (VHT) related cost (i.e., the cost associated with fleet size 𝐶 𝑓,𝑚 𝑓 , the cost associated with driver salary 𝐶 𝑓,𝑚 ℎ , and idling cost 𝐶 𝑓,𝑚 𝑑 ); Vehicle Kilometer Traveled (VKT) related cost (i.e., the cost associated with distance under cruising regime 𝐶 𝑓,𝑚 𝑐 , coasting regime 𝐶 𝑓,𝑚 𝑐𝑜 , and acceleration and deceleration regimes 𝐶 𝑓,𝑚

	𝐶 𝑢,𝑚 = 𝐶 𝑎,𝑚 + 𝐶 𝑤,𝑚 + 𝐶 𝑣,𝑚 ,	(1a)
	𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}

𝑢,𝑚 + 𝐶 𝑜,𝑚 + 𝐶 𝑃,𝑚 , (1) 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖} 𝑎𝑑 ). The operator cost is thus given by: 𝐶 𝑜,𝑚 = 𝐶 𝑠,𝑚 + (𝐶 𝑓,𝑚 𝑓 + 𝐶 𝑓,𝑚 ℎ + 𝐶 𝑓,𝑚 𝑑 ) + (𝐶 𝑓,𝑚 𝑐 + 𝐶 𝑓,𝑚 𝑐𝑜 + 𝐶 𝑓,𝑚 𝑎𝑑 ),

  bus's per-kilometer dwell time (hour) with 𝑟 ∈ {𝑒𝑏, 𝑤𝑏}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}; 𝜃 𝑣 : the value of in-vehicle time (dollar per hour);

		𝑟=𝑒𝑏,𝑤𝑏	𝑖=𝑝𝑘,𝑜𝑝	𝐿 0	,	(20)
	𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} ;
	Where
	𝑃 𝑟,𝑖 (𝑥): the number of passenger load at point x in direction r, in period i (passengers per hour); 𝑡 𝑚,𝑟,𝑖 𝑣 (𝑥): bus's per-kilometer travel time (hour) with 𝑟 ∈ {𝑒𝑏, 𝑤𝑏}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑚 ∈
	{𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖};
	𝑡 𝑚,𝑟,𝑖 𝑑	(𝑥):

• 𝑇 𝑖 • (𝑡 𝑚,𝑟,𝑖 𝑣 (𝑥) + 𝑡 𝑚,𝑟,𝑖 𝑑 (𝑥)) • 𝜃 𝑣 𝑑𝑥

  The cost associated with fleet size is closely related to the maximum fleet size required, which is correspondent to peak hour fleet size, denoted as 𝐵 ℎ . 𝐵 ℎ is determined as the quotient of peak hour cycle time 𝑡 𝑝𝑒𝑎𝑘 𝑐 and peak hour headway ℎ 𝑝𝑒𝑎𝑘 . Thus: 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} ; Where 𝜃 𝑐𝑜 : the unit cost per kilometer traveled by coasting (dollar per vehicle per kilometer); 𝜃 𝑎𝑑 : the unit cost per kilometer covered at accelerating from bus station or braking to bus station (dollar per vehicle per kilometer); Pollutant Cost In this model, we primarily take HC, CO, and NOx into account. The n th pollutant volume can be determined as the product of emission rate for pollutant n (n=1: HC; n=2: CO; n=3: NOx) and the per-kilometer travel time of different driving regimes, as expressed below: 𝑒 𝑛 𝑣 , 𝑒 𝑛 𝑐𝑜 , 𝑒 𝑛 𝑏 , and 𝑒 𝑛 𝑑 represent the emission rates for pollutant n, under acceleration, cruising, coasting, deceleration, and standing regimes, respectively, in unit of (ton/hour). Therefore, the pollutants cost generated while buses are operating on the corridor is: 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏}, 𝑛 ∈ {1,2,3} ; where, 𝜃 𝑝,𝑛 is the unit vehicle-related damage cost of pollutant ($/ton).Model OptimizationIn this model, the objective function is the generalized cost, which is the sum of user cost, operator cost, and pollutant cost. The expression of minimization problem is: min 𝛿 𝑚,𝑟 (𝑥),ℎ 𝑖 ,𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) {𝐶 𝑢,𝑚 + 𝐶 𝑜,𝑚 + 𝐶 𝑃,𝑚 } (32) Subject to:

	𝐶 𝑓,𝑚 𝑎𝑑 = 𝜃 𝑎𝑑 ∑	∑ 𝑄 𝑖 ≤ 𝑖=𝑝𝑘,𝑜𝑝 𝑟=𝑒𝑏,𝑤𝑏 𝑐𝑎𝑝 𝑏𝑢𝑠 ∫ 0 𝐿 ℎ 𝑖	𝑇 𝑖 ℎ 𝑖	• ( 2𝑎 𝑣 𝑣 𝑐,𝑖 2	+	𝑣 𝑐𝑜,𝑚,𝑟,𝑖 2 2𝑑 𝑣	(𝑥) )	• 𝛿 𝑚,𝑟 (𝑥)𝑑𝑥,	(29)
	(𝑎𝑙 𝑟,𝑖 (𝑥) + 𝑏𝑜 𝑟,𝑖 (𝑥)) • ℎ 𝑖 ≤ 𝑐𝑎𝑝 𝑠𝑡𝑜𝑝 • 𝛿 𝑚,𝑟 (𝑥)
	{	0 ≤ ℎ 𝑖 , 0 ≤ 𝛿 𝑚,𝑟 (𝑥), 0 ≤ 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) ≤ 𝑣 𝑐,𝑖
	𝐶 𝑓,𝑚 ℎ = 𝜃 ℎ ∑	𝑖=𝑝𝑘,𝑜𝑝	𝑇 𝑖 ℎ 𝑖	• 𝑡 𝑖 𝑐	,	(25)
	𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝} ;
	Where 𝜃 ℎ is the hourly salary for driver (dollar per hour). The cost associated with idling 𝐶 𝑚𝑓 𝑑 is presented as:
	𝐶 𝑓,𝑚 𝑑 = 𝜃 𝑑 ∑	𝑟=𝑒𝑏,𝑤𝑏	∑	𝑖=𝑝𝑘,𝑜𝑝	∫ 0 𝐿	𝑇 𝑖 ℎ 𝑖	• 𝑡 𝑚,𝑟,𝑖 𝑑	(𝑥)	𝑑𝑥	,	(26)
	𝑃 𝑚,𝑟,𝑛 = 𝑒 𝑛 𝑎 • 𝑡 𝑎,𝑚,𝑟,𝑖 𝑣	(𝑥) + 𝑒 𝑛 𝑣 • 𝑡 𝑐,𝑚,𝑟,𝑖 𝑣	(𝑥) + 𝑒 𝑛 𝑐𝑜 • 𝑡 𝑐𝑜,𝑚,𝑟,𝑖 𝑣	(𝑥) + 𝑒 𝑛 𝑏 • 𝑡 𝑏,𝑚,𝑟,𝑖 𝑣	(𝑥) + 𝑒 𝑛 𝑑 • 𝑡 𝑚,𝑟,𝑖
	cruising distance 𝑡 𝑚,𝑟,𝑖,𝑐 𝑣	𝑇 𝑖 ℎ 𝑖 (𝑥) • 𝑣 𝑐,𝑖 , and the per vehicle-kilometer cost 𝜃 𝑐 . And the cost related is , the per vehicle-kilometer
	expressed as:
	𝐶 𝑃,𝑚 = ∑	3 𝑛=1		∑	𝑟=𝑒𝑏,𝑤𝑏	∑	𝑖=𝑝𝑘,𝑜𝑝	∫ 𝑃 𝑚,𝑟,𝑛 • 𝜃 𝑝,𝑛 • 𝐿 0	𝑇 𝑖 ℎ 𝑖	𝑑𝑥,	(31)
	𝐶 𝑓,𝑚 𝑓 = 𝜃 𝑏 • 𝐵 ℎ 𝐶 𝑓,𝑚 𝑐 = 𝜃 𝑐 ∑ 𝑟=𝑒𝑏,𝑤𝑏		∑	𝑖=𝑝𝑘,𝑜𝑝	∫ 0 𝐿	𝑇 𝑖 ℎ 𝑖	• 𝑡 𝑚,𝑟,𝑖,𝑐 𝑣	(𝑥) • 𝑣 𝑐,𝑖	𝑑𝑥	,	(22) (27)
	𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} ;
	𝐵 ℎ = Where, 𝜃 𝑐 is the vehicle cost per unit distance covered at cruising speed (dollar per vehicle per 𝑡 𝑝𝑒𝑎𝑘 𝑐 ℎ 𝑝𝑒𝑎𝑘 kilometer).	(23)
			Similarly, the cost associated with distance coasted 𝐶 𝑓,𝑚 𝑐𝑜 , the cost associated with
	acceleration and deceleration 𝐶 𝑓,𝑚 𝑎𝑑 are expressed as follows, respectively:
	𝑡 𝑖 𝑐 = ∑	𝑟=𝑒𝑏,𝑤𝑏	∫ (𝑡 𝑚,𝑟,𝑖 𝑣 𝐿 0	(𝑥) + 𝑡 𝑚,𝑟,𝑖 𝑑	(𝑥))𝑑𝑥,	(24)
	𝐶 𝑓,𝑚 𝑐𝑜 = 𝜃 𝑐𝑜 ∑	𝑟=𝑒𝑏,𝑤𝑏	∑	𝑖=𝑝𝑘,𝑜𝑝	∫ 0 𝐿	𝑇 𝑖 ℎ 𝑖	•	𝑣 𝑐,𝑖 2 -𝑣 𝑐𝑜,𝑚,𝑟,𝑖 2 2𝑐 𝑣	(𝑥)	(28)

𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} Where, T presents the total operating time of a stop (hour). 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} ; 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏} ; Where 𝜃 𝑑 is the per hour cost of idling per vehicle(dollar per vehicle per hour); Then, we discuss the VKT related cost: The cost associated with distance travelled by buses at cruising speed for daily bus flow is presented as the integral of the product of vehicle flow over day • 𝛿 𝑚,𝑟 (𝑥)𝑑𝑥 𝑑 (𝑥), (30) 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏}, 𝑛 ∈ {1,2,3} ; where 𝑒 𝑛 𝑎 ,

  𝑎𝑙 𝑟,𝑖 (𝑥)+𝑏𝑜 𝑟,𝑖 (𝑥))•𝑇 𝑖 •𝜃 𝑎 The first order condition is applied to obtain the unconstrained optimal 𝑣 ̃𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) as follows:

	𝑖=𝑝𝑘,𝑜𝑝	𝑇 𝑖 ℎ 𝑖	ℎ 𝑝𝑘	+ 𝜃 𝑓 +
	𝜃 0 𝑇 ;
	𝑖=𝑝𝑘,𝑜𝑝 𝑓 2,𝑚,𝑟,𝑖 (𝑥)+∑ 𝑖=𝑝𝑘,𝑜𝑝 [𝑓 3,𝑚,𝑟,𝑖 (𝑥)+𝜃 𝑐 •𝑓 4,𝑚,𝑟,𝑖 (𝑥)]• + ∑ ∑ 𝜃 𝑝,𝑛 •𝑇 𝑖 ℎ 𝑖 • 𝑖=𝑝𝑘,𝑜𝑝 𝑛=1,2,3 (𝑓 5,𝑚,𝑟,𝑖 (𝑥)+ 𝑒 𝑛 𝑣 𝑣 𝑐,𝑖 •𝑓 4,𝑚,𝑟,𝑖 (𝑥)) 𝑇 𝑖 ℎ 𝑖 ) 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏}, 𝑛 ∈ {1,2,3} ; 4𝑣 𝑎 ( Where 𝑓 1,𝑚,𝑟,𝑖 (𝑥) = ( 1 𝑐 𝑣 -1 𝑑 𝑣 ) • 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 2 (𝑥) 2𝑣 𝑐,𝑖 -( 1 𝑐 𝑣 -1 𝑑 𝑣 ) • 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) + 𝑣 𝑐,𝑖 2𝑎 𝑣 + 𝑓 3,𝑚,𝑟,𝑖 (𝑥) = 𝜃 𝑑 • 𝑡 𝑑 (𝑥) + 𝜃 𝑐𝑜 • 𝑣 𝑐,𝑖 2 -𝑣 𝑐𝑜,𝑚,𝑟,𝑖 2 (𝑥) 2𝑐 𝑣 + 𝜃 𝑎𝑑 ( 𝑣 𝑐,𝑖 2 2𝑎 𝑣 + 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 2 (𝑥) 𝑣 𝑐,𝑖 2𝑐 𝑣 2𝑑 𝑣 ) ; 𝑓 4,𝑚,𝑟,𝑖 (𝑥) = -( 𝑣 𝑐,𝑖 2 2𝑎 𝑣 + 𝑣 𝑐,𝑖 2 -𝑣 𝑐𝑜,𝑚,𝑟,𝑖 2 (𝑥) 2𝑐 𝑣 + 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 2 (𝑥) 2𝑑 𝑣 ) ; 𝑓 5,𝑚,𝑟,𝑖 (𝑥) = 𝑒 𝑛 𝑎 • 𝑣 𝑐,𝑖 𝑎 𝑣 + 𝑒 𝑛 𝑐𝑜 • 𝑣 𝑐,𝑖 -𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) 𝑐 𝑣 + 𝑒 𝑛 𝑏 • 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) 𝑑 𝑣 + 𝑒 𝑛 𝑑 • 𝑡 𝑑 (𝑥) ; It should be mentioned that the stop density function is different on two sides of corridor to (34) reflect the flexibility of locating curbside bus stops. The expressions of optimal headway in peak hour and off-peak hour are as follows, respectively: ℎ 𝑝𝑘 = √ ∑ ∫ [(𝜃 𝑏 +𝜃 ℎ •𝑇 𝑝𝑘 )(𝑡 𝑚,𝑟,𝑝𝑘 𝑣 (𝑥)+𝑡 𝑚,𝑟,𝑝𝑘 𝑑 (𝑥))+𝑔 1,𝑚,𝑟,𝑝𝑘 (𝑥)+∑ 𝑇 𝑝𝑘 •𝜃 𝑝,𝑛 3 𝑛=1 •𝑃 𝑚,𝑟,𝑛 (𝑥)]𝑑𝑥 𝐿 0 𝑟=𝑒𝑏,𝑤𝑏 ∑ ∫ 𝑏𝑜 𝑟,𝑝𝑘 (𝑥) 𝐿 0 • 𝑇 𝑝𝑘 2 •𝜃 𝑤 +𝑃 𝑟,𝑝𝑘 (𝑥)•𝑇 𝑝𝑘 •𝑔 2,𝑚,𝑟,𝑝𝑘 (𝑥)•𝜃 𝑣 𝑟=𝑒𝑏,𝑤𝑏 𝑑𝑥 (35) ℎ 𝑜𝑝 = √ ∑ ∫ [𝜃 ℎ •𝑇 𝑜𝑝 •(𝑡 𝑚,𝑟,𝑜𝑝 𝑣 (𝑥)+𝑡 𝑚,𝑟,𝑜𝑝 𝑑 (𝑥))+𝑔 1,𝑚,𝑟,𝑜𝑝 (𝑥)+∑ 𝑇 𝑜𝑝 •𝜃 𝑝,𝑛 3 𝑛=1 •𝑃 𝑚,𝑟,𝑛 (𝑥)]𝑑𝑥 𝐿 0 𝑟=𝑒𝑏,𝑤𝑏 ∑ ∫ 𝑏𝑜 𝑟,𝑜𝑝 (𝑥) 𝐿 0 • 𝑇 𝑜𝑝 2 •𝜃 𝑤 +𝑃 𝑟,𝑜𝑝 (𝑥)•𝑇 𝑜𝑝 •𝑔 2,𝑚,𝑟,𝑜𝑝 (𝑥)•𝜃 𝑣 𝑟=𝑒𝑏,𝑤𝑏 𝑑𝑥 (36) 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏}, 𝑛 ∈ {1,2,3} ; Where 𝑔 1,𝑚,𝑟,𝑖 (𝑥) = 𝑇 𝑖 • 𝜃 𝑐 • 𝑡 𝑐,𝑚,𝑟,𝑖 𝑣 (𝑥) • 𝑣 𝑐,𝑖 + 𝜃 𝑎𝑑 • ( 𝑣 𝑐,𝑖 2 2𝑎 𝑣 + 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 2 (𝑥) 2𝑑 𝑣 ) • 𝛿 𝑚,𝑟 (𝑥) • 𝑇 𝑖 + 𝑇 𝑖 • 𝜃 𝑑 • 𝑡 𝑚,𝑟,𝑖 𝑑 (𝑥) + 𝑇 𝑖 • 𝜃 𝑐𝑜 • 𝑣 𝑐,𝑖 2 -𝑣 𝑐𝑜,𝑚,𝑟,𝑖 2 (𝑥) 2𝑐 𝑣 • 𝛿 𝑚,𝑟 (𝑥) ; 𝑔 2,𝑚,𝑟,𝑖 (𝑥) = 𝑚𝑎𝑥(𝑏𝑜 𝑟𝑖 (𝑥) • 𝑡 𝑚 ( 1 𝑐 𝑣 -1 𝑑 𝑣 )•𝑢 𝑟,𝑖 (𝑥)+∑ 𝑇 𝑖 ℎ 𝑖 •𝜃 𝑝,𝑛 •( 𝑒 𝑛 𝑐𝑜 𝑐 𝑣 -𝑒 𝑛 𝑏 𝑑 𝑣 ) 3 𝑛=1 ( 1 𝑐 𝑣 -1 𝑑 𝑣 )•[ 𝑢 𝑟,𝑖 (𝑥) 𝑣 𝑐,𝑖 + 𝑇 𝑖 ℎ 𝑖 •(𝜃 𝑐 -𝜃 𝑐𝑜 +𝜃 𝑎𝑑 +∑ 𝑒 𝑛 𝑣 𝑣 𝑐,𝑖 • 3 𝑛=1 𝜃 𝑝,𝑛 )] (37) 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏}, 𝑛 ∈ {1,2,3} ; 𝑏 , 𝑎𝑙 𝑣 ̃𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) = Where 𝑢 𝑟,𝑖 (𝑥) = 𝜃 𝑣 • 𝑇 𝑖 • 𝑃 𝑟,𝑖 (𝑥) + 𝜃 𝑏 ℎ 𝑝𝑘 𝑇 𝑖 + 𝜃 ℎ • ℎ 𝑖

; 𝑓 2,𝑚,𝑟,𝑖 (𝑥) = ∑ (𝜃 𝑣 • 𝑇 𝑖 • 𝑃 𝑟,𝑖 (𝑥) +

• 𝜃 ℎ ) • (𝑓 1,𝑚,𝑟,𝑖 (𝑥) + 𝑡 𝑑 (𝑥)) + 𝜃 𝑏 (𝑓 1,𝑚,𝑟,𝑝𝑘 (𝑥)+𝑡 𝑑,𝑝𝑘 (𝑥)) 𝑟𝑖 (𝑥) • 𝑡 𝑚 𝑎 ). Once the optimal value of headway is obtained, we can determine 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥).

The No.7 bus route, Yaan (China) TABLE 1 Cost Parameters of Four Transit Modes Supercharge Cost Parameters

	𝑎𝑙 𝑟,𝑖 (𝑥)+𝑏𝑜 𝑟,𝑖 (𝑥))•𝑇 𝑖 •𝜃 𝑎 [𝑓 3,𝑚,𝑟,𝑖 (𝑥)+𝜃 𝑐 •𝑓 4,𝑚,𝑟,𝑖 (𝑥)]• 𝑖=𝑝𝑘,𝑜𝑝 𝑖=𝑝𝑘,𝑜𝑝 4𝑣 𝑎 (𝑓 2,𝑚,𝑟,𝑖 (𝑥)+∑ ∫ [(𝜃 𝑏 +𝜃 ℎ •𝑇 𝑝𝑘 )(𝑡 𝑚,𝑟,𝑜𝑝 𝑣 (𝑥)+𝑡 𝑚,𝑟,𝑜𝑝 𝑑 (𝑥))+𝑔 1,𝑚,𝑟,𝑝𝑘 (𝑥)]𝑑𝑥 𝑇 𝑖 ℎ 𝑖 ) 𝐿 0 𝑟=𝑒𝑏,𝑤𝑏 ∑ ∑ ∫ 𝑏𝑜 𝑟,𝑝𝑘 (𝑥) 𝐿 0 • 𝑇 𝑝𝑘 2 •𝜃 𝑤 +𝑃 𝑟,𝑝𝑘 (𝑥)•𝑇 𝑝𝑘 •𝑔 2,𝑚,𝑟,𝑝𝑘 (𝑥)•𝜃 𝑣 𝑟=𝑒𝑏,𝑤𝑏 𝑑𝑥 ∑ ∫ [𝜃 ℎ •𝑇 𝑜𝑝 •(𝑡 𝑚,𝑟,𝑜𝑝 𝑣 (𝑥)+𝑡 𝑚,𝑟,𝑜𝑝 𝑑 (𝑥))+𝑔 1,𝑚,𝑟,𝑜𝑝 (𝑥)]𝑑𝑥 𝐿 0 𝑟=𝑒𝑏,𝑤𝑏 ∑ ℎ 𝑝𝑘 = √ ℎ 𝑜𝑝 = √ ∫ 𝑏𝑜 𝑟,𝑜𝑝 (𝑥) 𝐿 0 • 𝑇 𝑜𝑝 2 •𝜃 𝑢 𝑟,𝑖 (𝑥) 𝑣 𝑐,𝑖 + 𝑇 𝑖 ℎ 𝑖 •(𝜃 𝑐 -𝜃 𝑐𝑜 +𝜃 𝑎𝑑 ) 𝑣 𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) = mid(0, 𝑣 ̃𝑐𝑜,𝑚,𝑟,𝑖 (𝑥), 𝑣 𝑐,𝑖 ), Parameters Value Source $I-S infrastructure stop cost ($/stop/h) $0.47 Derived from Gu et al (2016), with additional (39) , (40) (42) (43) construction cost 2,590,000 yuan for charging facilities. Operating Costs (Distance) Maintenance cost per veh-km $0.012 Shu Tong Transpotation Agency Energy cost per km ($/km) $0.088 Electricity price 2018 $v, Cost per veh-km ($/veh-km) $0.1 Operating Costs (Time) Employees per vehicle 1.5 Average wage ($/h) $6.15 Yaan City average wage standard Labour cost per hour $9.225 Vehicle cost ($) $257,353 Vehicle lifespan (years) 8 Depreciation cost per hr ($/hr) $6.3 Assumed straight-line depreciation, work 14 hr per day CNG Cost Parameters Parameters Value Source $I-S infrastructure stop cost ($/stop/h) $0.35 Derived from Gu et al (2016) Operating Costs (Distance) Maintenance cost per veh-km $0.02 Shu Tong Transpotation Agency Energy cost per km ($/km) $0.238 CNG price 2018 $v, Cost per veh-km ($/veh-km) $0.258 Operating Costs (Time) Employees per vehicle 2 Average wage ($/hr) $6.15 Yaan City average wage standard Labor cost per hour $12.3 Vehicle cost ($) $73,529 Vehicle lifespan (years) 8 Depreciation cost per hr ($/hr) $1.8 Assumed straight-line depreciation, work 14 hr per day Lithium-ion battery Bus Cost Parameters Parameters Value Source $I-S infrastructure stop cost ($/stop/h) $0.58 Derived from Gu et al (2016), with an additional cost 5,000,000yuan for supplement equipment Operating Costs (Distance) Maintenance cost per veh-km $0.242 Shu Tong Transpotation Agency Energy cost per km ($/km) $0.088 Electricity price 2018 $v, Cost per veh-km ($/veh-km) $0.33 Operating Costs (Time) Employees per vehicle 1.7 Average wage ($/hr) $6.15 Yaan City average wage standard Labour cost per hour $10.455 Vehicle cost ($) $235,294 $5.76 Assumed straight-line depreciation, work 14 hr per day Diesel Bus Cost Parameters Parameters Value Source $I-S infrastructure stop cost ($/stop/h) $0.35 Derived from Gu et al (2016) Operating Costs (Distance) Maintenance cost per veh-km $0.025 Shu Tong Transpotation Agency Energy cost per km ($/km) $0.512 Diesel price 2018 $v, Cost per veh-km ($/veh-km) $0.537 Operating Costs (Time) Employees per vehicle 2 Average wage ($/hr) $6.15 Yaan City average wage standard Labor cost per hour $12.3 Vehicle cost ($) $73,529 Vehicle lifespan (years) 8 Depreciation cost per hr ($/hr) $1.8 Assumed straight-line depreciation, work 14 hr per day Vehicle lifespan (years) Depreciation cost per hr ($/hr) In this analysis, four different vehicle types are considered, among

𝑤 +𝑃 𝑟,𝑜𝑝 (𝑥)•𝑇 𝑜𝑝 •𝑔 2,𝑚,𝑟,𝑜𝑝 (𝑥)•𝜃 𝑣 𝑟=𝑒𝑏,𝑤𝑏 𝑑𝑥 (41) 𝑣 ̃𝑐𝑜,𝑚,𝑟,𝑖 (𝑥) = 𝑢 𝑟,𝑖 (𝑥) 𝑚 ∈ {𝑆𝐶, 𝐶𝑁𝐺, 𝐿𝑖, 𝐷𝑖}, 𝑖 ∈ {𝑝𝑘, 𝑜𝑝}, 𝑟 ∈ {𝑒𝑏, 𝑤𝑏}, 𝑛 ∈ {1,2,3} ; NUMERICAL APPLICATION System Inputs In this section, we apply continuum model to the 7 th bus route, Yaan (City), China, where the first supercharge bus route is in operation. The studied corridor is approximately 11 km in length, 21 stops in the east direction and 22 stops in the west, as shown in Figure 1. The bus's peak-hour cruising speed and off-peak hour cruising speed are given by Shu Tong Transportation Agency. All the operator cost items are shown in

Table 1 as follows: FIGURE 1

TABLE 2 Emission Rates of Pollutants at Different Driving Cycles 12m China National IV CNG bus Pollutant

 2

		Idling	Acceleration	Deceleration	Constant velocity
	𝑵𝑶 𝑿 (g/s)	0.0036	0.0152	0.0071	0.0115
	HC(g/s)	0.0012	0.0034	0.0021	0.0027
	CO (g/s)	0.0211	0.0473	0.0303	0.0363
			12m China National IV Diesel Bus
	𝑵𝑶 𝑿 (g/s)	0.0226	0.0973	0.0503	0.0766
	HC (g/s)	0.0013	0.0022	0.0014	0.0018
	CO (g/s)

TABLE 3 System Characteristics of Current and Optimized 7 th route in Yaan

 3 Previously, we have discussed the optimal values of stop density functions, service headways (peak and off-peak hour), and system costs for different bus types. This part will focus on analyzing the optimal value of coasting speed in different periods (peak hour and off-peak hour) for four vehicle types and investigating the interrelationship of coasting regime on system cost. The obtimized profiles of coasting speed with respect to different vehicle types in peak/off-peak hours are presented as follows in Figure4,5, respectively.

		Supercharge Bus Route Design
	System metrics	Current corridor	Optimal results	Real results
	ℎ 𝑝𝑒𝑎𝑘 , min	9.50 (weighted)	7.76	7.76
	ℎ 𝑜𝑓𝑓-𝑝𝑒𝑎𝑘 ,min	9.64 (weighted)	8.12	8.12
	𝑆𝑝𝑎𝑐𝑖𝑛𝑔 𝑒𝑏 , km	0.52	0.65 (17 stops)	0.65
	𝑆𝑝𝑎𝑐𝑖𝑛𝑔 𝑤𝑏 ,km	0.55	0.61 (18 stops)	0.61
	𝐶 𝑢 , $/day	14,081.0	5,567.4	5,540.5
	𝐶 𝑂 , $/day	2,624.2	2,559.9	2,359.4
	𝑇𝐶 , $/day	16,706.2	8,127.3	7,899.9
	Cost Saving, %	na	51.3%	52.7%
	Difference% (CA and	na	2.8%
	discrete models)
		CNG Bus Route Design
	System metrics	Current corridor	Optimal results	Real results
	ℎ 𝑝𝑒𝑎𝑘 , min	9.50 (weighted)	6.55	6.55
	ℎ 𝑜𝑓𝑓-𝑝𝑒𝑎𝑘 ,min	9.64 (weighted)	10.68	10.68
	𝑆𝑝𝑎𝑐𝑖𝑛𝑔 𝑒𝑏 , km	0.52	0.65 (17 stops)	0.65 (17 stops)
	𝑆𝑝𝑎𝑐𝑖𝑛𝑔 𝑤𝑏 ,km	0.55	0.61 (18 stops)	0.61 (18 stops)
	𝐶 𝑢 , $/day	15,365.0	5,594.7	5,796.2
	𝐶 𝑂 , $/day	2,165	2,398.2	2,412.7
	𝐶 𝑃 , $/day	252.1	88.0	91.3
	𝑇𝐶 , $/day	17,782.1	8,080.9	8,300.2
	Cost Saving, %	na	54.6%	53.3%
	Difference% (CA and	na	2.7%
	discrete models)
		Lithium-ion Battery Bus Route Design
	System metrics	Current corridor	Optimal results	Real results
	ℎ 𝑝𝑒𝑎𝑘 , min	9.50 (weighted)	8.3	8.3
	ℎ 𝑜𝑓𝑓-𝑝𝑒𝑎𝑘 ,min	9.64 (weighted)	10.3	10.3
	𝑆𝑝𝑎𝑐𝑖𝑛𝑔 𝑒𝑏 , km	0.52	0.69 (16 stops)	0.69 (16 stops)
	𝑆𝑝𝑎𝑐𝑖𝑛𝑔 𝑤𝑏 ,km	0.55	0.65 (17 stops)	0.65 (17 stops)
	𝐶 𝑢 , $/day	14,320.0	5,911.1	6,054.6
	𝐶 𝑂 , $/day	3,223.1	2,818.0	2,880.2
	𝑇𝐶 , $/day	17,543.1	8,729.1	8,934.8
	Cost Saving, %	na	50.2%	47.8%

Coasting speed profiles in off-peak hour for four vehicle types 3

			33								28
						peak-hour coasting regime		eastbound								eastbound
			32						westbound					peak-hour coasting regime		westbound
											26
			31
	value of coasting speed,km/h	26 27 28 29 30							value of coasting speed,km/h	16 18 20 22 24
			25								14
			24	0	2	4	6	8	10	12	12	0	2	4	6	8	10	12
						location at the corridor,km						location at the corridor,km
											29
		36.5
									eastbound								eastbound
									westbound								westbound
			36
											28
		35.5
	value of coasting speed,km/h	33 33.5 34 34.5 35			off-peak hour coasting regime		value of coasting speed,km/h	23 24 25 26 27			off-peak hour coasting regime
		32.5
											22
			32
											21
		31.5
												0	2	4	6	8	10	12
				0	2	4	6	8	10	12
														location at the corridor,km
						location at the corridor,km
			28								22
									eastbound								eastbound
									westbound								westbound
											21
			27
		value of coasting speed,km/h	23 24 25 26							value of coasting speed,km/h	20 16 17 18 19			off-peak hour coasting regime
						off-peak hour coasting regime
											15
			22
											14
			21								13
				0	2	4	6	8	10	12		0	2	4	6	8	10	12
						location at the corridor,km						location at the corridor,km

TABLE 4 Optimal Values of Coasting Speed in Different Periods on Two Directions

 4

	Vehicle Types	𝒗 𝒄𝒐,𝒆𝒃 in peak	𝒗 𝒄𝒐,𝒆𝒃 in off-peak	𝒗 𝒄𝒐,𝒘𝒃 in peak	𝒗 𝒄𝒐,𝒘𝒃 in off-peak
		(km/h)	(km/h)	(km/h)	(km/h)
	Supercharge	35.23	36.12	35.42	35.00
	CNG	25.00	23.07	25.57	22.80
	Lithium-ion	28.79	24.95	29.23	24.71
	Diesel	20.52	16.81	21.14	16

TABLE 5 System Cost with Coasting Regime vs. System Cost Without Coasting Regime Supercharge Bus Route Design

 5 This part will analyze the life cycle cost for four vehicle types, which is from a macroscopic view. From Table

	System metrics	No coasting regime	With coasting regime
	ℎ 𝑝𝑒𝑎𝑘 , min	7.59	7.76
	ℎ 𝑜𝑓𝑓-𝑝𝑒𝑎𝑘 ,min	8.94	8.12
	𝑆𝑝𝑎𝑐𝑖𝑛𝑔 𝑒𝑏 , km	0.50	0.65
	𝑆𝑝𝑎𝑐𝑖𝑛𝑔 𝑤𝑏 ,km	0.48	0.61
	𝐶 𝑢 , $/day	4,850.2	5,567.4
	𝐶 𝑂 , $/day	2,762.2	2,559.9


Clement Jonquet
email: jonquet@lirmm.fr

Paea Lependu

Sean Falconer

Adrien Coulet

Natalya F Noy

Mark A Musen

Nigam H Shah

NCBO Resource Index: Ontology-based search and mining of biomedical resources

Keywords:

The volume of publicly available data in biomedicine is constantly increasing. However, these data are stored in different formats and on different platforms. Integrating these data will enable us to facilitate the pace of medical discoveries by providing scientists with a unified view of this diverse information. Under the auspices of the National Center for Biomedical Ontology (NCBO), we have developed the Resource Index -a growing, large-scale ontology-based index of more than twenty heterogeneous biomedical resources. The resources come from a variety of repositories maintained by organizations from around the world. We use a set of over 200 publicly available ontologies contributed by researchers in various domains to annotate the elements in these resources. We use the semantics that the ontologies encode, such as different properties of classes, the class hierarchies, and the mappings between ontologies, in order to improve the search experience for the Resource Index user. Our user interface enables scientists to search the multiple resources quickly and efficiently using domain terms, without even being aware that there is semantics ''under the hood.''

Introduction

Researchers in biomedicine produce and publish enormous amounts of data describing everything from genomic information and pathways to drug descriptions, clinical trials, and diseases. These data are stored on many different databases accessible through Web sites, using idiosyncratic schemas and access mechanisms. Our goal is to enable a researcher to browse and analyze the information stored in these diverse resources. Then, for instance, a researcher studying allelic variations in a gene can find all the pathways that the gene affects, the drug effects that these variations modulate, any disease that could be caused by the gene, and the clinical trials that involve the drug or diseases related to that specific gene. The information that we need to answer such questions is available in public biomedical resources; the problem is finding that information.

The research community agrees that terminologies and ontologies are essential for data integration and translational discoveries to occur [START_REF] Bodenreider | Bio-ontologies: current trends and future directions[END_REF][START_REF] Butte | Finding disease-related genomic experiments within an international repository: first steps in translational bioinformatics[END_REF][START_REF] Goble | State of the nation in data integration for bioinformatics[END_REF]. However, the metadata that describe the information in data resources are usually unstructured, often come in the form of free-text descriptions, and are rarely labelled or tagged using terms from ontologies that are available for the domains. Users often prefer labels from ontologies because they provide a clear point of reference during their search and mining tasks [START_REF] Moskovitch | A comparative evaluation of full-text, concept-based, and context-sensitive search[END_REF][START_REF] Spasic | Text mining and ontologies in biomedicine: making sense of raw text[END_REF][START_REF] Sneiderman | Knowledge-based methods to help clinicians find answers in medline[END_REF]. For example, researchers and curators widely use the Gene Ontology to describe the molecular functions, cellular location, and biological processes of gene products. These annotations enable the integration of the descriptions of gene products across several model organism databases [START_REF] Rhee | Use and misuse of the gene ontology annotations[END_REF].

However, besides these examples, semantic annotation of biomedical resources is still minimal and is often restricted to a few resources and a few ontologies [START_REF] Shah | Comparison of concept recognizers for building the Open Biomedical Annotator[END_REF]. Usually, the textual content of these online resources is indexed (e.g., using Lucene) to enable querying the resources with keywords. However, there are obvious limits to keyword-based indexing, such as the use of synonyms, polysemy, lack of domain knowledge. Furthermore, having to perform keyword searches at each Web site individually makes the navigation and aggregation of the available information extremely cumbersome, if not impractical. Search engines, like Entrez (www.ncbi.nlm.nih.gov/Entrez), facilitate search across several resources, but they do not currently use as many of the available and relevant biomedical ontologies.

The National Center for Biomedical Ontology (NCBO) Resource Index addresses these two problems by [START_REF] Bodenreider | Bio-ontologies: current trends and future directions[END_REF] providing a unified index of and access to multiple heterogeneous biomedical resources; and (2) using ontologies and the semantic representation that they encode to enhance the search experience for the user. The NCBO BioPortal -an open library of more than 200 ontologies in biomedicine [START_REF] Noy | BioPortal: ontologies and integrated data resources at the click of a mouse[END_REF] -serves as the source of ontologies for the Resource Index. We use the terms from these ontologies to annotate, or ''tag,'' the textual descriptions of the data that reside in biomedical resources and we collect these annotations in a searchable and scalable index (Fig. 1). The key contributions to the field are (i) to build the search system for such an important number of ontologies and resources and (ii) to use the semantics that the ontologies encode.

In the context of our research, we call data element any identifiable entity or record (e.g., document, article, experimentation report) which belongs to a biomedical data resource (e.g., database of articles, experiments, trials). Usually, an element has an identifier and can be linked by a URL. For instance, the trial NCT00924001 is an element of the ClinicalTrials.gov data resource that can be accessed with: http://clinicaltrials.gov/ct2/show/NCT00924001. We call annotation -a central component -a link from an ontology term to a data element, indicating that the data element refers to the term either explicitly or not [START_REF]Annotation for the Semantic Web[END_REF][START_REF] Uren | Semantic annotation for knowledge management: requirements and a survey of the state of the art[END_REF]. We then use these annotations to ''bring together'' the data elements.

We currently index 22% resources, which are maintained by a variety of different institutions, with terms from more than 200 ontologies included in BioPortal (Appendix A). As of January 2011, our 1.5 Tb MySQL database, which stores the annotations in the Resource Index, contains 11 Billion annotations, 3.3 Million ontology concepts, and 3.2 Million data elements. The user interface is available at http://bioportal.bioontology.org/resources.

A preliminary version of the system was presented in [START_REF] Shah | Ontologydriven Indexing of Public Datasets for Translational Bioinformatics[END_REF]. In this paper, we illustrate use case scenarios (Section 2), describe the system implementation (Section 3) and the details of the indexing workflow (Section 3.3), and the different means to access the Resource Index (Section 3.4). We demonstrate how semantic technologies enable information retrieval and mining scenarios that were not possible otherwise (Section 4).

Use case scenarios

We will describe the functionality of the Resource Index through three use case scenarios.

Scenario 1: Multiple-term search across resources. The user is interested in the role of tumor protein p53 in breast cancer. He can search the Resource Index for ''Tumor Protein p53'' AND ''Breast Carcinoma'' as defined in the NCI Thesaurus (Fig. 2). The search results summarize the number of elements per resources anno-tated with both terms. The user can see there is relevant data linking p53 to breast cancer in such resources as ArrayExpress, ClinicalTrials.org, Gene Expression Omnibus (GEO), Stanford Microarray Database (SMD) and others. He can access the data elements within each resource quickly and navigate between resources.

Scenario 2: Exploratory search across resources. A researcher studying the causes and treatments for stroke in humans is interested in learning more about the genetic basis of the response to related conditions by searching the literature. She already knows that some related conditions such as stroke, tran-Fig. 1. NCBO Resource Index overview. We process each biomedical resource using the ontology-based indexing workflow. We store the resulting annotations in a database and make them available in several formats via REST Web services. BioPortal provides userfriendly interfaces to search and navigate the Resource Index. Fig. 2. Resource Index user interface. The search for resources that contain both ''Tumor Protein p53'' AND ''Breast Carcinoma.'' sient ischemic attack, and cerebral bleeding fall under the general category of cerebrovascular accidents (Fig. 3). Therefore, she starts by typing ''cerebr'' and immediately gets feedback in the form of suggested terms from various ontologies. She selects and initiates a search for Cerebrovascular Accident from the National Cancer Institute (NCI) Thesaurus. She notices a number of hits from several resources and drills down to read more about the data elements from both the GEO and Database of Genotypes and Phenotypes (dbGAP) resources. She focuses on GEO: the tag cloud emphasizes other terms that are ranked highly in these 31 elements. Thus, she can get an idea of what these elements are about. She selects ''Stroke'' in the tag cloud, then ''Treatment,'' and gets to the 12 elements that are annotated with the three previous terms. A similar series of steps on dbGAP leads her to two elements annotated with ''Cerebrovascular Accident,'' ''Stroke,'' and ''Physiology.'' As a result of her search, she has quickly located gene-expression data (from rats) that is connected to genotype-phenotype data (from humans). In rats, researchers studied the gene-expression level response to both stroke and to drugs used to treat stroke. In humans, researchers studied genotypes that predispose humans to stroke and affect the physiology of the outcome.

Scenario 3: Semantically enriched search across resources. The user wants to search gene expression data about ''retroperitoneal neoplasms.'' A direct keyword search with ''retroperitoneal neoplasm'' on the GEO Web site will return no results. However, there are several datasets in GEO about ''pheochromocytoma'' and ''renal cell cancer'' both of which are retroperitoneal neoplasms and thus relevant to the previous search. When our user queries the Resource Index with ''retroperitoneal neoplasm,'' he will get the results that use the hierarchy represented in the BioPortal ontologies. Specifically, the NCI Thesaurus defines ''pheochromocytoma'' as a subclass of ''retroperitoneal neoplasm.'' Thus, the user will get all data elements that are annotated with ''pheochromocytoma'' as a response to the query on ''retroperitoneal neoplasm,'' including the relevant resources in GEO. Furthermore, he also gets results from ArrayExpress and SMD, which are other repositories of gene expression data also indexed in the Resource Index.

In the next section, we describe the implementation of the Resource Index, which enables these use cases.

The NCBO Resource Index

To create the Resource Index, we process metadata describing data elements in a variety of heterogeneous resources to create semantic annotations of these metadata. We use the publicly available biomedical ontologies in BioPortal as a source of terms, their synonyms, and the relations between terms (Section 3.1). We use resource-specific access tools to process metadata that describe data elements in different resources (Section 3.2). We use an offthe-shelf concept-recognition tool to identify terms from BioPortal ontologies within the textual metadata and annotate, or tag, the corresponding element with the recognized terms. We expand these annotations using available ontology knowledge (Section 3.3). Finally, the Web services and user interface provide users with fast and scalable access to this index and support different use cases such as information retrieval and mining (Section 3.4).

Ontologies in the NCBO BioPortal

BioPortal, an open library of biomedical ontologies [START_REF] Noy | BioPortal: ontologies and integrated data resources at the click of a mouse[END_REF], provides uniform access to the largest collection of publicly available bio-Fig. 3. Searching the Resource Index in BioPortal. The user searches for resources on ''cerebrovascular accidents'' and finds gene-expression data that are relevant to different types of cerebrovascular accidents, such as stroke. medical ontologies. At the time of this writing, there are 245 ontologies in this collection. BioPortal users can browse, search, visualize, and comment on ontologies both interactively, through a Web interface, and programmatically, via Web services. The majority of BioPortal ontologies were contributed by their developers directly to BioPortal. A number of ontologies come from Open Biomedical Ontologies (OBC) Foundry [START_REF] Smith | The OBO foundry: coordinated evolution of ontologies to support biomedical data integration[END_REF], a collaborative effort to develop a set of interoperable ontologies for biomedicine. Bio-Portal also includes publicly available terminologies from the Unified Medical Language System (UMLS), a set of terminologies which are manually integrated and distributed by the United States National Library of Medicine [START_REF] Bodenreider | The unified medical language system (UMLS): integrating biomedical terminology[END_REF]. BioPortal includes ontologies that are developed in a variety of formats, including OWL, RDF(S), OBO (which is popular with many developers of biomedical ontologies), and RRF (which is used to distribute UMLS terminologies). BioPortal provides a uniform set of REST Web services to access basic lexical and structural information in ontologies represented in these heterogeneous formats.

We use the BioPortal REST services to traverse the ontologies and to create a dictionary of terms to use for direct annotations of data elements in biomedical resources. We use preferred name and synonym properties of classes for this dictionary. Some ontology formats have preferred name and synonym properties as part of the format (e.g., OBO and RRF). For OWL, ontology developers can either use the relevant SKOS properties to represent this information, or specify in the ontology metadata which are the properties that they use for preferred names (e.g., rdfs:label) and synonyms. Currently, our dictionary contains 6,835,997% terms, derived from the 3,349,338% concepts from 206% ontologies (the subset of BioPortal ontologies that are usable for annotation). We identify each concept by a URI defined in the original ontology or provided by NCBO.

Accessing biomedical resources

In addition to the ontology terms, the data elements from the biomedical resources are another major source of information for the Resource Index (Fig. 1). As of January 2011, we have indexed 22% public biomedical resources of different sizes (up to 3.2 Million elements and 1.4 Gb of data). We provide a list of sample resources in Appendix A. Data resources provide their data in idiosyncratic formats (often XML) and offer different means of access (often Web services). To access the information in the resources, we build a custom wrapper for each resource. The wrapper extracts the fields describing the data elements within a resource as illustrated in step 1 of Fig. 4. In developing each wrapper, we work with a subject matter expert to determine which textual metadata fields (later called contexts) we must process (e.g., title, description). We also assign each context a weight [0, 1] representing the importance of the field. We later use this weight to score annotations. 1 For example, we may give annotations appearing in the title a higher weight based on the expert's recommendation for that resource. In some cases, resources already tag elements with ontology terms, so the wrapper directly extracts the curated annotations and applies an appropriate weight. We call these annotations reported annotations. For example, the description of gene-expression data in GEO contains an organism field where a domain expert manually puts a term from the National Center for Biotechnology Information taxonomy, which refers to the relevant organism.

Our resource-specific wrappers access the data elements incrementally, enabling us to process only the data elements that were Fig. 4. Example of annotations generated for a GEO element. Direct annotations are generated from textual metadata and already existing ontology references of the data element. Then, expanded annotations are created using the ontology is_a hierarchy. Finally, all the annotations are aggregated and scored taking into consideration their frequency and context. 1 Researchers have previously demonstrated the influence and importance of the original context in which a term appears on information retrieval [START_REF] Moskovitch | A comparative evaluation of full-text, concept-based, and context-sensitive search[END_REF].

added to the resource since the last time that we processed the resource.

Ontology-based annotation

After we access the data elements describing the resource, we perform the following steps to create annotations for the data elements in the resource: (a) direct annotation with ontology terms; (b) semantic expansion of annotations; (c) aggregation and scoring of annotations (Fig. 4).

(a) Creation of direct annotations. We process each textual metadata using a concept-recognition tool that detects the presence of concepts in text. Our workflow accepts different concept recognizers ranging from simple string matching techniques to advanced natural language processing algorithms. We currently use Mgrep [START_REF] Xuan | Interactive medline search engine utilizing biomedical concepts and data integration[END_REF][START_REF] Dai | An efficient solution for mapping free text to ontology terms[END_REF] which enables fast and efficient exact matching against a very large set of input strings (however without any advanced natural language processing (e.g., stemming, permutation, morphology)). Concept recognizers usually use a dictionary. The dictionary (or lexicon) is a list of strings that correspond to preferred names and synonyms of ontology concepts. At this step, Mgrep uses the 6.8 Million terms dictionary built before. In the example in Fig. 4, the recognizer identifies the terms melanoma, melanocyte, and cell and creates a set of direct annotations with the corresponding concepts in the Human Disease, Cell type, and BIRNLex ontologies. We preserve the identified term, the context in which it appears, and its character position as provenance information about the annotation.

(b) Semantic expansion of annotations. After direct annotations step, several semantic-expansion components leverage the knowledge in the ontologies to create expanded annotations from the direct annotations.

First, the is_a transitive closure component traverses an ontology subclass-superclass hierarchy using a customized algorithm to create new annotations with superclasses of the classes that appear in direct annotations. We used the subclass transitive relation as defined by the original ontology e.g., is_a (OBO), rdfs:subClassOf (OWL) and abstracted by BioPortal to compute the transitive closure on the whole ontology graph. For instance, we will expand a direct annotation of a data element with the concept melanoma from NCI Thesaurus, to annotations with melanocytic neoplasm, cancer, and cellular proliferation disease because NCI Thesaurus defines melanoma as a subclass of melanocytic neoplasm, which in turn is a subclass of cellular proliferation disease (Fig. 4). We preserve the shortest ancestor level (direct parent, grandparent, etc.) as provenance information to use for scoring annotations. Naturally, the farther away the ancestor term is from the term in the direct annotation, the less relevant the corresponding expanded annotation is.

Second, the ontology-mapping component creates new annotations based on existing mappings between ontologies. BioPortal provides point-to-point mappings between terms in different ontologies. Some of these mappings were defined manually and some were created automatically using various mapping algorithms [START_REF] Noy | Collecting community-based mappings in an ontology repository[END_REF]. 2 We use the mappings that BioPortal stores and provides to expand our annotations and we do not follow them transitively. For instance, if a text is directly annotated with the concept treatment in Medical Subject Headings (MeSH), the mapping component will generate a new annotation with the concept therapeutic procedure from Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) because there is a mapping between these two terms in BioPortal. We preserve the type of mapping as provenance information to use for scoring annotations. It allows to score those expanded annotations proportionally to the mapping confidence (e.g., owl:sameAs, skos:exactMatch, skos:close-Match, manually curated or automatically generated).

(c) Annotation aggregation and scoring. We use the provenance information that we collect in creating direct and expanded annotations to assign each annotation a weight from 0 to 10 representing its relevance. For example, a match based on a preferred label gets a weight of 10 versus a synonym, which gets an 8; a match originating from a mapping gets a weight of 7 whereas one from an is_a relationship get a diminishing weight based on ancestor level. Because several annotations with the same concept but with different provenance and context can co-exist we aggregate all those annotations of an element to a unique pair [concept-element], called aggregated annotation, to which a score is assigned. Those are the annotations used for searches. The scoring algorithm takes into account frequency, provenance and context of the annotation by doing the sum of the weights assigned to each annotation normalized by the weights of the original contexts.

At each step, the annotation workflow populates several relational tables and stores the detailed (direct & expanded) and aggregated annotations. Because both ontologies and resources are changing often, we need to automatically update the Resource Index tables regularly. The workflow handles (i) resource updates (i.e., incremental processing of new elements added to resources) using wrappers that pull only the data elements that have not been processed yet and (ii) ontology updates (i.e., incremental processing of new ontologies and new ontology versions) because BioPortal provides version specific identifiers for ontologies. For simplicity, when a new ontology version is added to BioPortal, the previous annotations associated with the ontology are removed from the Resource Index and new ones are added. The indexing workflow has been specifically optimized for this to occur rapidly [START_REF] Lependu | Optimize first, buy later: analyzing metrics to ramp-up very large knowledge bases[END_REF]. We run these two different updates respectively weekly and monthly.

Accessing the NCBO Resource Index

The annotation and the scores that we described in the previous section constitute the Resource Index. The index contains 3 Billion aggregated annotations and 11 Billion detailed annotations (10% direct 90% expanded) as illustrated by Fig. 5. We provide both a Web service access to the index and a special-purpose easy-touse graphical user interface, which enables domain experts to explore and analyze the information in the Resource Index.

The main Resource Index user interface, illustrated in Figs. 2 and3, is a search-based interface geared towards biomedical end-users. Users do not even need to be aware that semantic technologies are driving the user interface, and can use it through a simple search-box mechanism. As the user types in terms that she is interested in, she gets a list of auto-complete suggestions for the search terms and the source ontologies for these terms. Users can search data elements using AND and OR constructs. 3She is presented with a list of search results (as snippets) as well as a tag cloud of related terms (selected in the top 10 results) to help refine her search further. For each identified element, a user can see the details of the annotations highlighted in the original text and link back to the URLs of the original data elements.

Users can retrieve the content of the Resource Index programmatically by calling a Web service and specifying either ontology concepts or specific data elements that they are looking for. Specifically, we provide the following services:

1. For a given concept, obtain the set of elements in one or several resources annotated with this concept (e.g., GEO and ArrayExpress elements annotated with concept DOID:1909). 2. For several concepts, obtain the union or intersection of the set of elements annotated with these concepts (e.g., GEO and ArrayExpress elements annotated with both DOID:1909 and CL:0000148). 3. For a given data element, obtain the set of concepts in one or several ontologies annotating this element (e.g., NCI Thesaurus concepts annotating the GEO dataset GDS1965).

The first two information-retrieval services offer a unique endpoint to query several heterogeneous data resources and facilitate data integration (defined as view integration in Goble & Stevens [START_REF] Goble | State of the nation in data integration for bioinformatics[END_REF]). The third service supports the type of exploration that the original resource may have never supported. This use case enables users to gather more information about a data element that they have already identified.

When retrieving annotations for a given element, users can filter out annotations using several mechanisms, such as limiting results to annotations with specific UMLS semantic types, using only results that match the whole word in the query, disabling the results obtained by matching synonyms, or selecting the type of mapping used for expanding annotations. Users can retrieve annotations in several formats (text, tab delimited, XML, RDF and OWL). The results are ordered by the scores assigned during the indexing phase.

Discussion and related work

The Resource Index provides semantically-enabled uniform access to a large set of heterogeneous biomedical resources. It leverages the semantics expressed in the ontologies in several different ways:

Preferred names and synonyms: Many biomedical ontologies specify, as class properties, not only labels (preferred names) but also synonyms for the class names, which we use during annotation. For example, a keyword search of caNanoLab resource with ''adriamycin'' would normally obtain no results. However, because the ontologies that we use have defined ''doxorubicin'' as a synonym for ''adriamycin,'' the Resource Index retrieves all caNanoLab elements annotated with the term ''doxorubicin.'' Auto-complete: As users type a term into the search box, they receive immediate feedback giving both preferred names and synonyms for matching classes from different ontologies.

Hierarchies: We use subclass relations to traverse ontology hierarchies to create expanded annotations, therefore improving the recall of search on general terms. For example, a search with ''retroperitoneal neoplasm,'' will retrieve data annotated with ''pheochromocytoma'' (Section 2). Notice that subclass relationships are present in all ontologies thus enable to provide the same feature for all ontologies. Specific ontology relationships are not considered, although we acknowledge there are often useful on a per-ontology approach.

Mappings: We use BioPortal mappings to expand the set of annotations. For example, a search with the concept ''treatment'' from MeSH retrieves the elements annotated with ''therapeutic procedure'' in SNOMED-CT because there is a mapping between these two concepts in BioPortal. 4The use of ontologies significantly enhances recall of searches (i.e., more relevant data elements are retrieved) without affecting precision of the top results. Our aggregation and scoring addresses the issue of precision by ranking relevant results for the user e.g., the algorithm ranks the direct matches higher over the ones obtained via semantic expansion. Semantic disambiguation is not handled yet e.g., someone searching elements for ''Cell'' in NCI Thesaurus will obtain the elements mentioning the word 'cell' as the abbreviation of cell phone. However, given the characteristics of the resources indexed (biomedical databases as opposed to general Web sites) the issue has not come up in practice.

Because the goal of the Resource Index is to improve runtime information retrieval and data-mining tasks, we decided to precompute inferences with ontologies (i.e., is_a and mapping expansion) rather than to implement semantic query-expansion algorithms [START_REF] Bhogal | A review of ontology based query expansion[END_REF] that would have computed inferences dynamically but would have required longer response time. Our technical decisions in terms of design and architecture were often driven by benchmarking analysis and metrics [START_REF] Lependu | Optimize first, buy later: analyzing metrics to ramp-up very large knowledge bases[END_REF]. The indexing workflow execution times range from a couple of minutes for the small resources to more than a week for the biggest one. Because it is impossible to include in the Resource Index all possible biomedical resources, NCBO provides the ontology-based annotation workflow as a Web service [START_REF] Shah | Comparison of concept recognizers for building the Open Biomedical Annotator[END_REF], the NCBO Annotator, which allows researchers to annotate their text data automatically and get the annotations back. They can use this service to develop their own semanticsearch applications. Researchers at the Medical College of Wisconsin have already created one such application for mining associations between gene expression levels and phenotypic annotations for microarray data from GEO (cf. http://gminer.mcw.edu).

Semantic annotation is an important research topic in the Semantic Web community [START_REF]Annotation for the Semantic Web[END_REF]. Tools vary along with the types of documents that they annotate (e.g., image annotation [START_REF] Hollink | Semantic annotation of image collections[END_REF]). For an overview and comparison of semantic annotation tools the reader may refer to the study by Uren and colleagues [START_REF] Uren | Semantic annotation for knowledge management: requirements and a survey of the state of the art[END_REF].

As we have mentioned earlier, our annotation workflow can be configured to use any concept-recognition tool. A number of publicly available concept recognizers identify entities from ontologies or terminologies in text. These recognizers include IndexFinder [START_REF] Zou | IndexFinder: a method of extracting key concepts from clinical texts for indexing[END_REF], SAPHIRE [START_REF] Hersh | SAPHIRE -an information retrieval system featuring concept matching, automatic indexing, probabilistic retrieval, and hierarchical relationships[END_REF], CONANN [START_REF] Reeve | CONANN: an online biomedical concept annotator[END_REF], and the University of Michigan's Mgrep [START_REF] Xuan | Interactive medline search engine utilizing biomedical concepts and data integration[END_REF]. The National Library of Medicine (NLM)'s MetaMap [START_REF] Aronson | Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program[END_REF], which identifies UMLS Metathesaurus concepts in text, is generally used as the gold standard for evaluating tools in the biomedical domain. Many of these tools are not under active development and are restricted to a particular ontology or the UMLS.

Related tools in the biomedical domain include Terminizer [START_REF] Hancock | Terminizer -assisting mark-up of text using ontological terms[END_REF], which is an annotation service similar to the NCBO Annotator. Terminizer recognizes concept names and synonyms and their possible permutations but only for OBO ontologies. Terminizer does not allow any automatic semantic expansion of the annotations but allows refining annotations using broader or narrower terms in the user interface. Whatizit [START_REF] Rebholz-Schuhmann | Text processing through Web services: calling Whatizit[END_REF], which is a set of text mining Web services that can recognize several types of entities such as protein and drug names, diseases, and gene products. Reflect [START_REF] Pafilis | Reflect: augmented browsing for the life scientist[END_REF], which highlights gene, protein, and small-molecule names and can perform the recognition in HTML as well as PDF and MS Word documents. The originality of Reflect, when used in a Web browser, is that the tool links the identified terms to corresponding entries in biomedical resources e.g., UniProt, DrugBank. However, the tool is not driven by ontologies and does not execute any semantic expansion.

We have conducted a comparative evaluation of two concept recognizers used in the biomedical domain -Mgrep and Meta-Map -and found that Mgrep has clear advantages in large-scale service oriented applications, specifically addressing flexibility, speed and scalability [START_REF] Shah | Comparison of concept recognizers for building the Open Biomedical Annotator[END_REF]. The precision of concept recognition varies depending on the text in each resource and type of entity being recognized: from 93% for recognition biological processes in descriptions of gene expression experiments to 60% in clinical trials, or from 88% for recognizing disease terms in descriptions of gene expression experiments to 23% for PubMed abstract [START_REF] Shah | Comparison of concept recognizers for building the Open Biomedical Annotator[END_REF]. Other studies reported similar results [START_REF] Simon | Using the NCBO Web services for concept recognition and ontology annotation of expression datasets[END_REF][START_REF] Sarkar | Leveraging biomedical ontologies and annotation services to organize microbiome data from mammalian hosts[END_REF]. The average precision is approximately 73%, average recall is 78%.

Most of the other annotation tools do not perform any semantic expansion, which gives the Resource Index and the Annotator a significant advantage. There are however other tools in the biomedical domain that use semantics internally including Medico-Port [START_REF] Can | MedicoPort: a medical search engine for all[END_REF], which uses UMLS semantics to expand user queries; the work of Moskovitch and colleagues [START_REF] Moskovitch | A comparative evaluation of full-text, concept-based, and context-sensitive search[END_REF], who use ontologies for annotation (concept based search) and demonstrate the importance of the context (context-sensitive search) when annotating structured documents. HealthCyberMap [START_REF] Kamel-Boulos | A first look at HealthCyberMap medical semantic subject search engine[END_REF] uses ontologies and semantic distances for visualizing biomedical resources information. Essie [START_REF] Ide | Essie: a concept-based search engine for structured biomedical text[END_REF] shows that a judicious combination of exploiting document structure, phrase searching, and concept based query expansion is useful for domain optimized information retrieval. Finally, other studies such as Khelif and colleagues [START_REF] Khelif | An ontology-based approach to support text mining and information retrieval in the biological domain[END_REF] illustrate the annotation of a specific resource with specific ontologies (the GeneRIF resource annotated with UMLS and Galen in this case).

Currently, we create annotations based only on textual fields. However, we can extend our approach to other kinds of documents (i.e., images, sounds) by changing the tool that we use for concept recognition. We currently process only text meta-data in English. However, as BioPortal now contains ontologies in multiple languages, we can start using concept recognizers for other languages in the future.

Challenges and future plans

We are currently working on expanding the Resource Index to include more resources. Our goal is to index up to 100 public resources, including PubMed, which provides access to all research articles in biomedicine (approximately 20 Million elements). We have analyzed the metrics on ontologies in order to re-structure the database backend for the Resource Index. This restructuring has enabled us to reduce the processing time for one of our larger datasets from one week to one hour [START_REF] Lependu | Optimize first, buy later: analyzing metrics to ramp-up very large knowledge bases[END_REF]. With this type of optimizations, we can now annotate extremely large datasets such as PubMed. We have already indexed the last five years of it (20%). We note that since 2010, changes in MetaMap allow it to be deployed with ontologies outside of UMLS. We are investigating the possibility of including MetaMap as an alternative concept recognizer in the annotation workflow.

One limiting factor in increasing the number of resources that we index is the need to develop custom access tools for most resources. However, most resource access tools follow the same principles, so we have built templates that enable our collaborators to build them easily and quickly to process their own datasets and to include them in the Resource Index.

Our next challenge is to evaluate the user interface and to understand what works best for domain experts. We have performed small-scale formative evaluations, but will need to work on larger scale evaluation, with different groups of users.

Conclusions

We have presented an ontology-based workflow to annotate biomedical resources automatically as well as an index constructed using this workflow. Ontology-based indexing is not new in biomedicine, however it is usually restricted to indexing a specific resource with a specific ontology (vertical approach). We adopt a horizontal approach, accessing annotations for many important resources using a large number of ontologies. This approach follows the translational bioinformatics and Semantic Web vision to discover new knowledge by recombining already existing knowledge (i.e., resources and ontologies) in a manner that the knowledge providers have not previously envisaged.

The Resource Index enables domain experts to search heterogeneous, independently developed resources. While we use ontologies and semantics ''under the hood'' to improve the quality of the results and to simplify the user interaction, the users are not aware of this complexity. They use a simple search-box interface

Fig. 5 .

 5 Fig. 5. Number and types of annotations in the Resource Index.

Table 1

 1 A sample of ontologies included in the Resource Index. Please refer to http://bioportal.bioontology.org/ontologiesurlhttp://bioportal.bioontology.org/ontologies for a complete listing.

	Ontology	Maintained By	Format	# Classes
	NCI Thesaurus (NCIt)	National Cancer Institute	OWL	80 K
	Medical Subject Headings (MSH)	National Library of Medicine	RRF	223 K
	Gene Ontology (GO)	GO Consortium	OBO	33 K
	Systematized Nomenclature of Medicine-Clinical Terms	International Health Terminology Standards Development	RRF	391 K
	(SNOMEDCT)	Organisation
	Medical Dictionary for Regulatory Activities (MDR)	Maintenance and Support Services Organization	RRF	69 K
		The Jackson Laboratory	OBO	3 K

RadLex (RID)

Radiological Society of North America PROTEGE 30 K International Clasification of Diseases(ICD10) World Health Organization RRF 12 K NCBI organismal clasification (NCBITaxon) National Center for Biotechnology Information OBO 513 K Mouse adult gross anatomy (MA)

Table 2 A

 2 sample of resources included in the Resource Index. Please refer to http://rest.bioontology.org/resource_index/resources/list/urlhttp://rest.bioontology.org/ resource_index/resources/list/ for a complete listing. Resource/contexts indexed Maintained By Elements Gene Expression Omnibus (GEO) gene expression and molecular abundance repository /title, summary, organism National Center for Biotechnology Information (NCBI) 23,287 (21 Mb) ArrayExpress (AE) microarray data and geneindexed expression profiles /name, description, species, experiment_type European Bioinformatics Institute (EMBL-EBI) 16,444 (23.4 Mb) caNanoLab (CaNano) biomedical nanotechnology research results /Composition, Association, Method, etc. Adverse Event Reporting System Data (AERS) adverse events reported to FDA by doctors and other professionals /Drug_char, Drug_names, Drug_admin_route Clinical Trials (CT) reports on clinical research in human volunteers /title, description, condition, intervention

	National Cancer Institute's cancer Nanotechnology	890 (19.1 Mb)
	Lab
	AersData.org	1,172,881
		(278.4 Mb)
	ClinicalTrials.gov	101,606 (187.3 Mb)

UniProt KB (UPKB) protein sequence and functional information /genesymbol, goAnnotationList, proteinName UniProt.org 18,461 (4.2 Mb)

C. Jonquet et al. / Web Semantics: Science, Services and Agents on the World Wide Web 9 (2011) 316-324

In this work we assume mappings between ontologies already exists, the creation of biomedical mappings is discussed in numerous other papers.

The OR construct is currently available only through Web service; it is not available through the graphical user interface.

Notice there is no composition of the semantic expansion components e.g., mapping ancestors are not used for annotations. C. Jonquet et al. / Web Semantics: Science, Services and Agents on the World Wide Web 9 (2011) 316-324

Acknowledgements

This work was supported in part by the National Center for Biomedical Ontology, under roadmap-initiative Grant U54 HG004028 from the National Institutes of Health. The NCBO Resource Index won the First prize in the Semantic Web Challenge 2010 (http://challenge.semanticweb.org/).

Appendix A. Lists of ontologies and resources

see Tables 1 and2.

Author's personal copy and can drill down on the specific resources that contain their terms of interest or any other relevant terms.