Automatic Train Control System Development and Simulation for ...

Feature 

Automatic Train Control System 

Development and Simulation 

for High-Speed Railways 

Abstract 

Research and development on high-speed railway 

systems and particularly its automatic control 

systems, are introduced. Numerical modeling of 

high-speed trains in the Chinese high-speed train 

system and its associate automatic control systems 

are described in detail. Moreover, modeling 

and simulation of train operation systems are 

analyzed and demonstrated. 

Hairong Dong, Bin Ning, Baigen Cai, 

and Zhongsheng Hou 

1. Introduction 

Since the first-ever high-speed train, the Japanese 

Tokaido Shinkansen, started operation at the 

maximum speed of 210 km/h in 1964 [1, 2], railway 

systems have gone through a rapid evolution and 

improvement with significant growths, mainly in Japan, 

France, Germany, Italy, Britain and, lately, China. 

Digital Object Identifier 10.1109/MCAS.2010.936782 

© DIGITAL VISION & 

WIKIMEDIA COMMONS 

6 IEEE CIRCUITS AND SYSTEMS MAGAZINE 1531-636X/10/$26.00©2010 IEEE SECOND QUARTER 2010

The world speed record today is 574.8 km/h achieved 

by the French TGV POS train Hunder test conditions on 

the railway network LGV Est, reported in 2007 [3]. Noticeably, 

the TGV record for the fastest scheduled rail 

journey with a start-to-stop average speed of 279.4 km/h 

was already surpassed by the Chinese CRH (China Railway 

High-speed) service, called Harmony Express, on 

the Wuhan–Guangzhou High-speed Railway in 2009. 

In recent years, the Chinese railway systems have 

gone through a massive phase of upgrading and expansion, 

especially after the establishment of the China Train 

Control System (CTCS) in 2002. Since the second CTCS 

level-3 Zhengzhou-Xi’an High-Speed Railway being put on 

service with the maximum speed of 352 km/h on 6 February 

2010, China has scored the top mark in high-speed 

railway development worldwide. It is estimated that by 

the end of 2020, China will have 18,000 km Dedicated Passenger 

Lines (DPL, another name for high-speed railway 

in China), with an operating speed of 350 km/h, which 

will cover most areas of the whole country. 

The next-generation Chinese railway systems will 

face with many challenges in related technologies. The 

coordinated operation of the high-speed and the conventional 

railway systems, as well as the interoperability 

among DPL, rebuilt existing lines and old operation 

lines, all need to be carefully examined, integrated and 

managed. These acquire products and services of highend 

technologies, which come along with accurate operational 

requirements and strict demands. What’s more, 

completing the construction of infrastructure and facility 

is not the end of the system development. To endure 

the overall system to operate properly, there must be a 

reliable high-level automatic control system embedded, 

so as to ensure the entire railway network to function 

safely, cost-effectively and highly efficiently. But this is 

never a local, low-cost and easy task. 

High-speed railway systems have brought up many 

new and challenging technical issues as well as commercial 

issues such as transport capacity, safety and comfort 

[4, 5]. Whether the railway systems can work with high 

security and high efficiency mainly depended on the core 

of the train control systems. Over the years, much effort 

has been made to study automatic train control systems 

[6–8], from the earlier Track-circuit Based Train Control 

(TBTC) to the modern Communication Based Train Control 

(CBTC) systems [9–11]. In the long past, conventional 

trains in China were manually controlled and operated by 

drivers based on trackside interlocking and blocking, in 

conjunction with various train signaling and surveillance 

devices, as well as human experience and skills. As the 

high-speed railway systems being rapidly developed in 

China recently, the old TBTC system has gradually been 

phased out, and replaced by the modern Train Control 

System (TCS), which has been built, tested and partially 

put in operation. TCS is a train protection system especially 

designed for high-speed railways, ensuring safe 

and smooth operations [12, 13]. The main advantages of 

ATC include making possible use of automatic signaling 

instead of trackside signaling, and possible use of smooth 

deceleration patterns in lieu of the rigid and rough stops 

encountered by the old train stopping technology. 

During the operation of a train, the vehicle dynamics 

are quite complicated, involving such as starting, traction, 

coasting, speeding, braking, and stopping, not to 

mention the complex states under different loading and 

weather conditions. Therefore, accurately modeling the 

train dynamics is by no means an easy task. The earlier 

single-particle model of a train ignored the length of the 

train and the reactions between two train compartments, 

hence was quite far apart from the real physical train mechanics 

and dynamics. Starting from the 1980s, by considering 

a multi-particle rigid-body model, namely, by taking 

into account the reaction forces between compartments, 

a general multi-compartment train model is considered 

more realistic. But this was at the price of computational 

complexity. For instance, in [17], a train with seven compartments 

was modeled by a set of totally 84 differential 

equations. When coming to high-speed trains, the situation 

is more complicated and even intractable: the train 

as a whole is a time-varying high-dimensional nonlinear 

dynamical system with rich and complex dynamical behaviors. 

As a result, accurate dynamical analysis of the 

train model becomes the first step-stone towards highspeed 

rail train systems control and operation. 

Whether or not a railway system can be operated 

safely and efficiently depends essentially on the performance 

of its Automatic Train Control (ATC) system. 

The train coasting system, in particular, utilizes real-time 

road information and online environmental data in combination 

with onboard references to achieve optimal control 

of the train traction and braking while keeping travel 

schedule on-time, reducing electric-energy consumption, 

and maintaining the journey comfort for passengers. 

Generally, in urban rail systems the ATC system includes 

Automatic Train Protection (ATP), Automatic 

Train Operation (ATO) and Automatic Train Supervision 

(ATS), as shown in Fig. 1 [14–15]. 

In Fig. 1, ATO is responsible for all the traction and 

braking controls, as well as parking and stopping operations, 

as further illustrated by Fig. 2 [16, 28]. Therefore, 

Hairong Dong, Bin Ning, Baigen Cai, and Zhongsheng Hou are with Beijing Jiaotong University, Beijing 100044, P. R. China. E-mail: hrdong@bjtu.edu.cn. 

SECOND QUARTER 2010 IEEE CIRCUITS AND SYSTEMS MAGAZINE 7

ATO is a key to, and has a direct impact on, the development 

of the train operation systems and automatic train 

control systems. 

Facing with such real-time multi-objective dynamic 

operational requirements, intelligent control strategies 

came into play in the 1980s. PID control, genetic algorithms 

[18, 19], fuzzy logic [20, 21], expert systems [22], and 

artificial neural networks [23, 24] were suggested and 

applied to vehicle optimal control, energy-consumption 

planning, precise parking, and accurate locomotive trajectory 

tracking [25]. More recently, iterative learning 

control and hybrid control approaches have received 

increasing interest [26–28]. As Electric Multiple Units 

(EMU) become more and more popular, data-based 

simulations and ATO algorithms for high-speed trains 

also attract more and more attention [29, 30]. 

Traction Service 

Brake 

Figure 1. Structure of automatic train control system. 

Speed 

Station Platform 

With the rapid expansion of the nationwide metropolitan populations, 

the Chinese government has a strong desire to improve the 

urban railway loading capacity. 

Emergency 

Brake 

ATS 

Ground Control 

ATP Limiting Speed 

Train Speed 

Speed Adjustment 

Point 

Tractor Cab 

ATP 

ATP 

Stopping 

Point 

Figure 2. Operation of the ATO system. 

ATO 

ATO 

Speed Adjustment 

Distance 

Position Stall 

Loop 

The CBTC-based ATC system adopts a Moving Blocking 

(MB) principle, which appears to be a future direction 

of automatic train control systems for high-speed 

trains. In fact, it has been implemented in some urban 

railway systems in China. Following the rapid development 

of a new-generation of wireless communication 

systems and technologies, the traditional Fixed Blocking 

(FB) devices will eventually be replaced by MB devices, 

which will thereby dominate most train ATC systems 

in the near future. Thus, it is a new challenge to 

develop a train operation and tracking model that can 

well describe the moving and tracking mechanisms of 

train operation in fast mode [31, 32]. This will effectively 

improve the railway system’s safety, efficiency and 

economic impacts. 

With the rapid expansion of the nationwide metropolitan 

populations, the Chinese government has a strong 

desire to improve the urban railway loading capacity. 

As mentioned above, the operation of a train is a very 

complex process which involves complicated motion 

dynamics such as starting, traction, coasting, speeding, 

braking, parking and stopping, under different loading 

and weather conditions. It is very desirable to establish 

a train operation simulation platform for design, testing 

and verification purposes, to carry out various traffic 

experiments under time-varying, complex and uncertain 

environmental conditions. This platform can effectively 

improve railway systems’ theoretical design and computer 

simulation before road testing and onside operations. 

Today, several rail system simulators and train-traction 

computational platforms have been built and put in use 

[33]. British Rail Research Division, for example, had 

designed GATTS in 1970, evolved to the so-called VISION 

and OSLO simulators afterwards, which are improved 

versions that take full advantage of modem computing 

technology. Other typical simulation systems include the 

RAILSIM Train Performance Calculator (TPC) systems 

and the TrainSTAR in USA, UTRAS in Japan, etc. These 

systems can carry out various train operation computations, 

delay and resumption analysis, signaling effect and 

prediction, and train capacity evaluation, among others. 

Noticeably, various high-performance train control 

simulation systems are under rapid development in 

China, which are generally designed to suit the characteristics 

and conditions of the Chinese developing highspeed 

railway systems and environments [34, 35, 77], as 

further discussed below. 

8 IEEE CIRCUITS AND SYSTEMS MAGAZINE SECOND QUARTER 2010

2. Numerical Modeling of High-Speed Trains 

A high-speed railway system is a typical complex 

dynamical system with large time-delays, high nonlinearity 

and multi-objectives, which is typically working 

under time-varying and uncertain conditions on 

such parameters as traction power supplies, different 

types of signals, speed restrictions, train tractions 

and braking, etc. It has many complicated nonlinear 

dynamical phenomena observed, including stochastic 

mechanical vibrations, micro-pressure fluid dynamics, 

sound explosion (sonic booms), fluctuating dynamical 

characteristics, and so on. 

In real operations, different situations imply different 

control demands for different purposes, while the main 

objectives are always on-time scheduling, high-speed 

motion, full-load and safe operation of the trains. Several 

important factors have to be taken into account, 

especially rail conditions, vehicle specifications, operational 

strategies, riding comfort of passengers, and 

energy consumption. Here, rail conditions include the 

distance between platforms, gradients, train parking 

and stopping points, and the dwelling time durations. 

Vehicle conditions include resistance driving, traction 

and braking effects, as well as specifications of the train 

set. Driving conditions, moreover, refer to the all-out 

driving mode, coasting driving mode, and speed restrictions. 

All things considered, therefore, it is generally 

very challenging to automatically control a train satisfactorily. 

Nevertheless, over the years, a great deal of 

effort has been made to understand and analyze most 

characteristics of such a complex dynamical system 

towards automation, and many insightful and useful results 

have been obtained. 

In the following, a specific numerical model of automatic 

train control is briefly discussed, which can describe 

many of the afore-mentioned characteristics and 

dynamical features of a high-speed train, and is suitable 

for control design. 

As mentioned, the high-speed train operation is very 

complicated and complex. A common practice is to perform 

numerical modeling based on the basic traction 

dynamics theory verified by using a large amount of real 

experimental data. Theoretically, the train operation 

function consists of three types of operation modes: 

traction mode, braking mode, and coasting mode. Taking 

into account the relations between these operation 

modes and the train motions, numerical modeling of a 

general high-speed train has been carried out, which 

enables the designer to compute the train speeds, traveling 

times, energy consumption, braking and stopping 

performances, and train operation forces, thereby suggesting 

correct parameter values and effective operations. 

With a good numerical model, the designer can 

study the motion stability, train operation, and emergency 

braking, and can optimize the train speeds, loading 

capacity and energy consumption, etc. 

There are many important issues to be considered in 

high-speed train modeling. When a high-speed train with 

initial speed 250–300 km/h is being stopped at emergency, 

it requires a high deceleration and consumes a large 

amount of power [36, 37]. For example, to stop the TGV-A 

train at the initial speed of 300 km/h requires an average 

of 1.0 m/s 2 deceleration change rate. For the forthcoming 

Beijing-Shanghai high-speed line, simulations have 

shown that using an air brake system to stop a train at 

initial speed of 300 km/h, it will take 4.1 km for the train 

to arrive at a complete stop, which will be 6.5 km if the 

initial speed is 350 km/h and 8.5 km if 380 km/h. Simulations 

have also shown that the total resistance, which 

the Beijing-Shanghai high-speed train will face with, is 

about 84% of the total that two CRH2-300 high-speed 

trains will normally encounter. This critical issue has a 

direct impact on the commercial capacity, design, construction 

and operation of high-speed trains and their 

associate railways, therefore must be taken into serious 

consideration in system modeling and analysis. 

In 1970, a comprehensive and manageable multi-particle 

train model was established by the American Railway 

Engineering Association [38]. In that model, in the 

traction mode the train is accelerated for propulsion and 

decelerated for braking. The train’s inertia is also used 

as a track condition and the train speed is incorporated 

in the coasting mode. The actual measurement data 

about the train traction-braking curve and resistance 

characteristic curve are used to analyze the running 

forces, which turns out to be quite different from the 

current running conditions of the conventional trains. 

Five kinds of CRH traction characteristic curves are 

illustrated by Fig. 3 [36, 37]. The intrinsic dynamics for 

the high-speed ATO system based on multi-running conditions 

is described by 

Q 2 Cv 1v2 2Ks 1 v 

Fh 5 • 

| 2v $j2 1traction : F1v2 5Q2 

2Cv1v2 2Ks 12j, v | 2v ,j2 1coasting : F 1v2 502 

2Cv1v2 2Ks2Fz 1v2 1v | 2v #2j21braking : F1v2 52Fz2. Here, v is the desired speed; v | is the actual speed; j is 

acceptable error between actual and desired speeds; v is 

the basic resistance; Q is the traction which is a function 

of speed and gear force determined by the type of train; 

F1v2 is the motion force exerted from the train; F z 1v2 is 

the braking force; C is a train weight constant; s is the 

driving distance; K is the track stiffness coefficient. 

It can be seen from Fig. 3 that the train speed is determined 

mainly by the train traction and braking forces, 

as well as the train motion and other resistances. 


EMU Traction (kN), Resistance (kN) 

320 

300 

280 

260 

240 

220 

200 

180 

160 

140 

120 

100 

80 

60 

40 

20 0 

CRH2-300 

Traction 

CRH2 Traction 

CRH1 Traction 

CRH5 Traction 

0 20406080100120140160180200220240260280300320340 

CRH3 Traction 

CRH1 Resistance 



Train Speed (km · h –1 ) 

CRH1 Traction/kN CRH1 Resistance/kN 


CRH2-300 Traction/kN CRH2-300 Resistance/kN 



Figure 3. CRH traction characteristic curves. 

CRH2-300 

Resistance 

CRH3 

Resistance 

High-speed train traction characteristic curves are 

linearly fitting curves described according to the actual 

running conditions, used to represent the relationship 

between speed and traction of the train [42]. They are 

used to derive the governing equations of motion of the 

high-speed ATO systems. Denote by 1v 1, F 1 2 and 1v 2, F 2 2 

two known points on the traction characteristic curve, 

by 1v x, F x 2 a point between 1v 1, F 1 2 and 1v 2, F 2 2, where F 

represents the force. Using a typical linear interpolation, 

the formula of traction is obtained as 

Fx 5 F1 1 1vx 2 v121F2 2 F12 . (2) 

v2 2 v1 During the train operation, this formula of traction is affected 

not only by the wheel-rail coupling, pantograph and 

catenary, but also by the fluid-solid coupling resistance 

existing through the coasting operation. The total resistance 

experienced by the train comes from all fronts: steel 

rails, bow net and the air. It is quite difficult to derive an 

accurate formula or equation to precisely describe such 

compound resistance characteristic of a high-speed train. 

In the earlier 1900s, Schmidt and Dunn proposed a 

sensible train-resistance model [40, 41], based on which 

Davis derived a now-famous train-resistance computational 

formula in 1926 [42]. Thereafter, the American 

Railways Engineering Association developed a comprehensive 

model in 1970, which can well describe the 

train motion characteristics [38]. Then, in 1982, a more 

realistic train-resistance model was proposed [43], 

which is still being used for computations and simulations 

today: 

v 1v2 5 a 1 bv 1 cv2 

e 

g1s2 5 lsin1u 1s2. 

10 IEEE CIRCUITS AND SYSTEMS MAGAZINE SECOND QUARTER 2010 

(3) 

Here, a, b and c are constants of the Locomotive 

type, determined empirically from real data; g1s2 is the 

resistance force in special sections of the railway such 

as ramps, curves and tunnels; u 1s2 is the slope at position 

s, referred to as gradient l is the additive slope. 

When the train achieving speeds of well over 200 km/h, 

especially achieving speeds of over 300 km/h, the running 

resistance mainly depends on the third parameter, 

c, in Davis’ formula. 

The train braking system plays a key role in safe 

operations of high-speed trains. Resistance force of 

a high-speed train under the condition of braking includes 

running resistance as well as regenerative braking, 

friction braking, and air braking forces. Thus, a 

good braking system model incorporating such forces is 

needed in order to meet various crucial performance requirements 

of the high-speed trains. In train modeling, 

energy consumption and passengers comfort are also 

important factors to be accounted for. 

In general, energy is supplied to a train by means 

of electricity or via combustion of diesel. The traction 

system in a train may convert such energy sources to 

mechanical energy. On the other hand, the energy used 

for regenerative braking may be converted into electric 

energy and be fed back to the pantograph and catenary, 

thus energy saving benefit is apparent. 

For commercial high-speed trains, passengers’ ride 

comfort against lateral mechanical oscillations has been 

a main concern, which actually has been discussed for 

a long time. Much research and development on train 

dynamics and improving human comfort have been conducted, 

by applying various control methods. A creep 

force for dynamic analysis of the wheel-rail tangential 

force of a high-speed train was derived in [44], aiming 

to improve running stability and ride comfort. A modelbased 

predictive control technique from a mixed H 2/Hinfinity 

control approach was suggested in [45] for active 

vibration control of a railway vehicle. Relationships between 

model parameters, physical parameters and ride 

quality are built within a unified “track-vehicle-human” 

model, which was verified experimentally by identifying 

both model parameters and responses. Moreover, power 

spectral densities were used to find ways to improve 

the ride comfort in [46]. 

A three-trailer train dynamic model was set up in 

[47], taking into consideration the effects of coupling 

stiffness and the damping coefficient in the concern of 

the passengers ride comfort. Lateral dampers mounted 

in between compartments can improve the lateral ride 

comfort effectively and can also improve the vertical

ide comfort to some extent [48]. A three-dimensional 

coupled dynamic model for motor cars and trailers was 

also suggested, based on the system dynamics of rigid 

bodies, in [49]. During the modeling process, various 

nonlinear factors have to be considered, including nonlinear 

wheel-rail contact geometry, nonlinear wheel-rail 

creep force and creepage, nonlinear force for couplers, 

nonlinear secondary suspension force, etc. It was shown 

that the lateral ride quality of the head compartment 

and the end compartment are generally worse than the 

middle ones [50]. 

Future train design is envisaged to be more cost-effective 

and more energy-efficient, with faster speed introduced 

and lighter bodies built. In recently years, different 

kinds of high-speed trains and their modeling have been 

carefully studied. One way to maximize the train speed 

is to use the existing infrastructure with the introduction 

of tilting trains. A tilting train is a train that could reduce 

the passenger lateral acceleration by leaning the train 

compartments inward on curves, which thereby enables 

higher train speeds. This idea attracts quite some interest 

for improving the regional express trains in Europe. 

Reduced-order robust controllers for tilting trains were 

proposed to improve the curving performance of highspeed 

trains. Linear quadratic Gaussian (LQG) control 

with loop transfer recovery was already applied to active 

suspension designs, where model reduction on high-order 

controllers were used to achieve desirable closedloop 

performances [51]. In order to study the dynamical 

behaviors of tilting trains with low computational costs, 

the basic mechanisms of the tilting train were described 

by some constrained equations. Simplified models were 

then proposed to account for the dynamical couplings 

between compartments subjected to the articulated 

configuration of the Talgo trains [52]. PID-fuzzy-logic 

multi-objective tuning approach was also proposed for 

improving the performance of tilting trains in [53]. Since 

building new high-speed railways is a very expensive 

mission, tilting trains are being seriously considered by 

many inland cities in western China. 

Numerical modeling of high-speed trains is useful not 

only for train’s ride control but also for ATC analysis, as 


3. Automatic Train Control Systems 

The train control system requires two sets of data to 

execute accurate train operation. One set is static data, 

such as rail parameters, locomotive traction force and 

braking capabilities, etc., which is closely related to the 

train model. Another is dynamic, such as train position, 

speed, and motion states. It is a key mission to obtain 

and provide the train control system with these two sets 

of data in real time. 

The train control system is the core of the entire 

framework of both numerical model and physical platform. 

The train control system is to continuously supervise, 

control and adjust the train operations, ensuring 

the train to operate safely at all times. It provides safe 

motion authority directly to the driver through cab-display 

and it continuously monitors the driver’s actions. 

With the increase of train speeds in railway systems, 

various railway technologies have seen significant 

changes and improvements. The China Train Control 

System (CTCS) is a train control system implemented 

since 2002, which is specified for compliance with the 

high-speed and the conventional interoperability and 

directives. The system has in effect remedied the current 

lack of standardization in signaling and control, 

which constitutes one of the few major obstacles to the 

development of high-speed rail traffic in China. 

CTCS by nature is an automatic train protection system, 

based on cab-signaling and signal aspects as well 

as continuous tracking to the data transmission on the 

train system. The movement authority (MA) and the corresponding 

line information are transmitted to the control 

unit of the train and then being displayed in the cab 

for the driver as commands or references. A train with 

complete CTCS equipment and functionality can operate 

on any CTCS line without much technical restriction. 

CTCS has several levels, namely CTCS level-0, 1, 2, 3 and 

4. The definition of the levels depends on how the line 

is equipped and the way the information is transmitted 

to the train. Interoperability is necessary for the train 

control system to achieve joint operation among DPL 

and rebuilt lines, where CTCS levels-2, 3 and 4 are backcompatible 

with lower levels. 

CTCS level-3 in China was firstly used on the Wuhan- 

Guangzhou High-speed Railway services, where trains 

have speeds up to 350 km/h on DPL, which was started 

operation in December 2009. It has two subsystems: 

ground subsystem and onboard subsystem. The ground 

subsystem includes balises, track circuits, a wireless 

communication network (GSM-R), and a Radio Block 

Centers (RBCs). The onboard subsystem, on the other 

hand, includes onboard devices and an onboard wireless 

module, as shown in Fig. 4 [37]. 

CTCS level-3 is functionally similar to the European 

Train Control System (ETCS) level-2, used by the European 

Commission for trains all over Europe in 1996. The 

CTCS level-3, however, is designed to suit the cross-line 

transport commercial requirements imposed in China, 

which contains CTCS level-2 as back-up for CTCS level-3. 

Different trains in operation under the control of CTCS 

level-3 is illustrated in Fig. 5 [37]. When RBC or the wireless 

communication system has faults or failures, CTCS 

level-3 will be switched to CTCS level-2 automatically, 


VC (Including 

CTCS–2/CTCS–3 

Control Logic) TCR Driver–Machine 

Interface (DMI) 

Tachometer Balise 

Figure 4. Structure of CTCS level-3. 

Radio Transmission 

Module 

as shown by Fig. 6 [37]. This allows other vehicles to 

enter high-speed railways if needed, thereby increasing 

the utilization of the system. In contrast, ETCS does 

not have such a back-up, therefore has to stop services 

whenever troubles and problems arise. Moreover, CTCS 

level-3 has track circuits, used to check the train occupation 

and safety. It also has ground balises, used to adjust 

the position calibration and motion directions. They can 

be switched in between CTCS level-2 and CTCS level-3, 

while ETCS-2 does not have such equipments either. 

Automation control of high-speed trains is executed 

by ATP: according to the travel scheduling and 

CTCS Level-2 Line 

CTCS Level-2 

CTCS Level-3 

ATP Equipment 

of CRH 

Track 

Circuit 

Transmission 

Module (BTM) 

Receiver 

GSM-R 

GSM-R 

Enchange Center 

Radar Sensor 

CTCS Level-3 

CTCS Level-2 

commands, traveling lines, and the train states, ATP 

computes the speed limits for the train. The CTCS level-3 

onboard devices will then link all such speed restriction 

profi les together so as to arrive at a distanceto-go 

speed control mode, as shown in Fig. 7 [37], which 

utilizes the reading data from the Balise along the line. 

CTCS level-3 adopts ATP with High Priority and 

Driver-Priority mode for train operation. The socalled 

ATP with High Priority mode refers to an automatic 

control mechanism which follows a predesigned 

speed-control profile in accordance with the real descending 

or stopping motion of the train, as shown in 

Fig. 8. CTCS level-3 adopts ATP 

with High Priority mode in Full 

Supervision mode, while useing 

Driver-Priority mode in Partial 

Supervision mode. 

As mentioned above, the ATO 

system is one important part of 

the ATC system, which can replace 

the driver to operate the 

train while ensuring the train to 

run efficiently. The most important 

function of the ATO system is 

speed adjustment. Based on differ- 

Balise 

ent blocking modes, the train operation 

system has different speed 

control curves. It can be easily 

imagine that the distance-to-go 

12 IEEE CIRCUITS AND SYSTEMS MAGAZINE SECOND QUARTER 2010 

TCC 

CTCS Level-3 Line 

Block 

Figure 5. Different trains in operation under the control of CTCS level-3. 

Radio 

Block Center 

LEU 

Balise 

TSR 

Server 

CTC 

ZPW-2000 

Track 

Circuit 

Computer-Based 

Interlocking

300–350 km/h Line 

CTCS-3 Area 

Balise 

200–250 km/h Line 

CTCS-2 Area 

ZPW2000 

Track Circuit 

Information Code 

L5 

200–250 km/h Line 

CTCS-2 Area 

Switching 

Point 

L5 L5 L4 L3 

Warning 

Point 

(Forward) 

(a) 

Warning 

Point 

(Backward) 

ZPW2000 

Track Circuit 

Information Code 

Warning 

Point 

(Forward) 

(b) 

300–350 km/h Line 

CTCS-3 Area 

Switching 

Point 

Figure 6. Transition between CTCS level-3 and CTCS level-2. 

(a) Transition CTCS level-2 to CTCS level-3. (b) Transition 

CTCS level-3 to CTCS level-2. 

speed control mode has the shortest braking distance 

in general. 

The study of high-speed ATO systems is of great importance, 

and many good ATO algorithms have been 

proposed and implemented in recent years, including for 

instance PID control, fuzzy-logic control, expert-system 

control and neural-network control, among others. PID 

control algorithms were first developed and applied into 

the ATO system for its precision on the London Tube 

in 1968. A fuzzy ATO controller was designed and recommended, 

which can automatically change the train 

operation mode to offset unforeseen deviations caused 

by various factors [55]; it can actually control the train’s 

departure, speed regulation, and station-stop time at a 

target point. Expert systems with artificial neural network 

were also suggested to train control systems [20]. 

Moreover, predictive fuzzy controller selects the most 

likely control command based on a prediction of control 

result and its control accuracy, train safety and 

passengers ride quality, which was shown to somewhat 

outperform the traditional PID control methods [23]. A 

genetic algorithm was applied to automatic train operations, 

which can find the best idling point that meets the 

L5 

Warning 

Point 

(Backward) 

L4 

L3 

Balise 

Figure 7. Distance-to-go speed control mode. 

energy efficiency standards [56]. Predictive fuzzy logic 

was explored in [57] for the ATO system to accurately 

track the automatic stopping curves. However, most of 

the above-mentioned research works were concerned 

SECOND QUARTER 2010 IEEE CIRCUITS AND SYSTEMS MAGAZINE 13 

Speed 

EB 

B7N 

B4N 

B1N 

Poor 

Braking Curve 

Speed 

Brake 

Key of Ease 

Speed 

EBP 

NBP 

WSPREL 

EBP 

NBP 

WSPREL 

S1 + S2 S3 S4 

Actual Speed 

Actual Speed 

Good 

Braking Curve 

Target 

Speed 

S 

Equipment 

Monitoring Curve 

Distance 

Distance 

Distance 

Figure 8. ATP with high priority mode. (a) Emergency 

braking mode. (b) Service brake. 

(a) 

(b)

with the ATO system only for traditional trains. Recently, 

for high-speed trains, a highly effective integrated ATO 

control algorithm has become a research focus. 

Energy-saving is another main concern for the 

high-speed train systems. In this regard, a genetic algorithm 

was used in [18] to simulate the ATO, which 

could suggest the best coasting point before the train 

starts according to different running situations, and 

differential evolution algorithm was applied to study 

the energy-saving issue in the train operation system. 

New directions of research on the ATO system were 

discussed in [58]. 

Noticeably, however, there has not been a systematic 

study on various control algorithms for the ATO systems 

in China to date, which is urgently needed. The Chinese 

railway research and services have been making great 

efforts to develop better intelligent control algorithms 

aiming at satisfying high-efficient, energy-saving, timeaccurate, 

safe and comfort requirements, including 

modern control techniques such as multi-layer fuzzyneural-network 

control and other approaches [60]. 

Today’s CBTC system is a new-generation integrated 

control system for high-speed trains, where the method 

of determining the location of a CBTC train is to examine 

the CBTC train (or vehicle) itself, to determine its 

location, direction, and speed, and then report such information 

to wayside equipments for coordination. The 

goal of the CBTC system is to realize closed-loop control 

onboard, for both fixed blocking and moving blocking 

devices, and to enhance the comprehensive transport 

capacity of the railway system. 

Train operation simulation systems have been developed 

to meet the need of the CBTC system research, as 


4. Train Operation Simulation System 

Today, there are more than ten countries and regions 

with commercial high-speed railways in operation, 

totaling more than ten thousand kilometers of railways 

worldwide. 

High-speed railways have been on the national plans 

of construction in many countries for their fast and 

large volume of transportation, energy-saving economic 

impacts, and safe and comfort services. Still, how to 

further improve the efficiency and safety of high-speed 

railways is a commonly concerned problem. 

As discussed above, high-speed train motion and operation 

is a complicated and complex system and process, 

involving locomotive dynamics, communications 

and signaling, rail tracks, etc., for which simulation systems 

provide a practical platform for design, testing and 

analysis. Due to the high complexity and diversity of 

automatic train control systems, good computer-based 

simulation tools, for evaluation of the effectiveness of 

new protocols and the feasibility of new applications, 

are of great importance. Initial research on rail vehicle 

dynamics simulation had processed towards better understanding 

of the stability, curving behavior and ride 

quality of high-speed trains. Much testing and validation 

experiences are now used as an essential part for 

the design process for new high-speed trains and for 

investigating services in the existing trains [33, 61]. 

British Rail Research designed its first train operation 

simulator in 1970. This powerful simulator, called 

GATTS, ran on a mainframe computer and was therefore 

less user-friendly and required complex skills and 

computer knowledge to set up and run a simulation. 

In 1987, VISION and OSLO simulators were developed 

as a new version, which took full advantage of the modem 

computing technology. VISION was designed as an 

easy-to-use simulator with the capability of modeling 

the systems that are currently in use or are planned to 

be built on British Railways in the near future. It is userfriendly, 

so train timetable and infrastructure planners 

who were not computer experts could also operate it. 

Other typical simulation systems include the RAILSIM 

Train Performance Calculator (TPC) systems developed 

in USA, which can perform important analysis and computations 

to predict the train travel schedules, evaluate 

the traction performances of rolling stock, compare 

different possible results in different train scheduling 

scenarios, calculate train start-and-stop parameters on 

longest and steepest ramps, as well as maximum loading 

and speed, and so on. Also, TrainSTAR is an engineers’ 

auxiliary system developed in USA, aiming at 

improving train operation, reducing energy consumption 

and increasing safety performance. Moreover, 

UTRAS was developed by a Japanese transportation 

laboratory, in 1990s, to be a general railway simulation 

system. This system can carry out various train 

operation computations, delays and resumption analysis, 

signaling effects and prediction, and train capacity 

evaluation, among others. 

As the high-speed railway facilities and services continuously 

expand worldwide, particularly in China with 

DPL in place, automatic train operation simulation systems 

have become a research focus for many institutes 

and universities. As a result, many simulation tools have 

been built, tested and applied. As one example, a train 

tracing model for the moving blocking system with different 

types of trains running was developed based on 

the cellular automaton concept in [31]. Colored Petri net 

tool is a useful modeling method for describing the structure 

and function of a system with graphs, which is particularly 

suitable for modeling and simulating large-sized 

asynchronous and concurrent systems [62]. A formal 


descriptive modeling for train tracking systems was suggested 

in [63, 65], and a centralized control system that 

can track two trains simultaneously was developed with 

a simulation platform in [64]. In the field of train operation 

control systems using Petri net, a layered model of 

ETCS using colored Petri net was proposed and simulated 

by CPN/tools [66]. Train operation behavior was 

1 Current status of wireless 

GSM-R, linked with 

Current location and status of 

onboard equipment, linked 

with related simulation 

modules. 

On-board 

Equipment 

Interlocking 

information, linked 

with interlocking 

simulation module. 

Interlock 

Simulation 

Equipment 

All Events 

Recorder 

GSM-R simulation 

module. 

Status of track circuit code, 

linked with STM simulation 

module. 

Testing and Simulation Platform 

Adjoining 

RBC 

Equipment for Testing 

TSR 

Information of balise, 

linked with balise 


CTC 


Multitrain 


Module 

GSM-R 


Real 

JRU Download 

Evaluation 

Module 

Data Record and Analysis 

Information of temporary 

speed restriction, 

linked with temporary 

TSR simulation. 

RBC Data 


Module 

GSM-R 


Real 

JRU Download 

Evaluation 

Module 

Result Generation 

and Evaluation 

Figure 9. Architecture of the CTCS level-3 simulation testing platform. 

modeled by a mixed Petri net in [67]. A colored Petri net 

was also developed to model the train control system 

of the Germany railways, which employs 168 layered 

networks and 2583 network elements to build a system 

model with three parts: onboard, RBC and environment 

[68]. In China, layered model of CTCS level-3 was built 

based on a colored Petri net, with basic function models 

Balise Data 


Module 

Balise 

Signal 

Generator 

STM 

TCR 

Simulator 

STM Decoding 

and 

Communication 

Module 

Display Control 

DMI Event 

Recorder 

SECOND QUARTER 2010 IEEE CIRCUITS AND SYSTEMS MAGAZINE 15 

Speed 

Interface 

Adaption 

TIU 

Interface 

Adaption 

RTM RTM BTM TCR 

ODO TIU 

Equipment for Testing (RBC) JRU JRU Equipment for Testing (VC/DMI) 

1 

5 

RBC 

Simulation Display Data 

Train information, 

linked with RBC (real or 

simulation). 

Information of adjoining RBC, 

linked with adjoining RBC 


Adjoining 

RBC 

Demo Interface for Simulation Test 

4 

TMS 

Scene Controller 

SC 3 

Speed 

Sensor 

Simulator 

SSS 

Static Data 

Management 

Module for 

Scene Data 

Testing and 

Management 

TIUS 

2 

Driver or 

Driving 

Platform 


DMI

of onboard system and RBC system, and several wireless 

working modes [69]. 

In the field of object-oriented UML-based modeling 

of train operation control systems, a objective-oriented 

method was proposed to model the railway signal system, 

and used to build a railway signal simulation platform 

in [70, 71]. A model for the train automatic control 

system was built based on object-oriented method in 

[72]. A design plan for the train automatic drive system 

from a model-based approach was proposed using a 

language-based information analysis technique in [73]. 

A formalized model of microcomputer interlock was designed 

and tested in [74]. 

Multi-resolution modeling has been a hot topic in the 

field of computer-aided modeling and simulation since 

1990s, and will be one of the few key technologies for 

distributed interactive simulations in the future. The 

National Research Council of USA considers multi-resolution 

modeling as one of the basic challenges in the 

modern modeling and simulation technology [75]. An 

application of multi-resolution modeling to urban railway 

systems simulation was also studied in [76]. 

In China, research on multi-resolution modeling is 

being carried out intensively, for example at Beijing Jiaotong 

University. For different purposes of applications, 

the task of system resolution classification is divided 

into RBC internal and external operations such as signaling 

and display between RBC and the train, as shown 

by Fig. 9 [77]. Along this line, many specific research 

projects have been planned and funded, and are being 

carefully performed. 

5. Conclusions 

Looking into the future, there are several key technologies 

to be addressed. 

First, better modeling of high-speed trains remains to 

be an important and yet challenging issue for research 

and development. The high-speed railway system is a 

hybrid system composing of elastic flexible body (catenary), 

multiple rigid-body (locomotive and compartments), 

continuous elastic body (railway tracks), sells 

(slab tracks), loosely accumulated materials (subgrade), 

and soil structures (the embankment), etc., which as a 

whole is subjected to air-fluid-solid media interactions 

with the train. Therefore, dynamical analysis on the 

high-speed train system has to go beyond the traditional 

modeling methods and take an integrated complex hybrid 

systems modeling approach. On the one hand, the 

model should contain several key components such as 

the train, rail, and air-fluid-soil subsystems. On the other 

hand, it should account for the impacts and effects of 

hybrid coupling of multi-body and multi-state factors. 

Based on a large amount of real data collected from a 

train travelling history, it is possible to establish a datadriving 

systematic modeling approach which, when theoretical 

modeling fails, provides a unique feasible framework 

in practice. 

Second, automatic high-speed train control systems 

are still to be further improved. CBTC is a promising future 

direction which, based on the valuable experiences 

gained from its simulation and utilization in urban railway 

systems, will provide good references and suggestions 

for the high-speed train systems development. 

Moreover, better and more powerful simulation 

platforms for high-speed train systems are to be developed 

based on the so-called ACP framework: Artificial 

systems, Computational experiments and Parallel execution. 

Similarly, better and more powerful automatic 

control systems are to be further improved based on 

such as ABC (agent-based control) [78] and ADP (adaptive 

dynamic programming) [79] principles. 

In summary, the next-generation Chinese railway systems 

will face with new challenges in constructional and 

control technologies. The global coordinated operation 

of the high-speed and conventional railway systems and 

their automatic control systems are demanding profound 

scientific theories and high-end technologies. All 

these together calls for new progress in high-speed railway 

systems design, implementation and maintenance, 

among other endeavors. 

Acknowledgments 

The authors acknowledge the support of NSFC Grants 

60870013, 60736047 and 60834001 and would like thank 

the reviewers for their useful and constructive suggestions 

which contributed to the improvement of the quality 

and presentation of this article. 

Hairong Dong received the B.S. and 

M.S. degrees in Automatic Control 

and Basic Mathematics from Zhengzhou 

University, Zhengzhou, in 1996 

and 1999, respectively, and the Ph.D. 

degree in General and Fundamental Mechanics 

from Peking University in 2002. 

She joined the School of Electronic and Information 

Engineering of Beijing Jiaotong University in 2002 and 

currently is an Associate Professor. She was a visiting 

scholar to the University of Southampton, UK (2006), 

University of Hong Kong (2008), and City University of 

Hong Kong (2009). In 2007, she served as Project Level-3 

Expert in the Department of Transportation of Beijing 

Organizing Committee for the Olympic Games. Her research 

interests include: stability and robustness of 

complex systems control theory and intelligent transportation 

systems. 


Bin Ning is a Professor in the School of 

Electronic and Information Engineering 

of Beijing Jiaotong University and 

also the President of the University. 

He was a visiting scholar to the Brunel 

University, UK, from 1991 to 1992, and 

University of California, Berkeley, from 

2002 to 2003. He directed many key national scientific 

projects in China. His scientific interests include: intelligent 

transportation systems, communication based 

train control, rail transport systems, system fault-tolerant 

design, fault diagnosis, system reliability and safety 

studies. He is a member of the IEEE and currently the 

Chair of the Technical Committee on Railroad Systems 

and Applications of the IEEE Intelligent Transportation 

Systems Society. 

Baigen Cai received the B.S. and M.S. 

degree in Traffic Information Engineering 

and Control from Beijing Jiaotong 

University in 1987 and 1990, respectively. 

He was a visiting scholar to the Ohio 

State University from 1998 to 1999. He 

joined Beijing Jiaotong University as an 

Assistant Professor in 1990 and currently is a Professor 

and vice-dean of the School of Electronic and Information 

Engineering. His research interests include: intelligent 

transportation systems, GNSS and its applications 

in transportations, multi-sensor fusion and integration, 

and intelligent control. He was responsible of several 

key national projects. He is an IEEE member and serves 

as reviewer for many international journals. 

Zhongsheng Hou received the B.S. and 

M.S. degrees in Applied Mathematics 

from Jilin University of Technology, 

Changchun, in 1983 and 1988, respectively, 

and the Ph.D. degree in control 

theory from Northeastern University, 

Shenyang, in 1994. He was a postdoctoral 

fellow at the Harbin Institute of Technology, Harbin, 

from 1995 to 1997, and a visiting scholar to Yale University, 

USA, from 2002 to 2003. In 1997, he joined Beijing 

Jiaotong University, where currently he is a Professor 

in the Advanced Control Systems Lab of the School of 

Electronic and Information Engineering. His research 

interests include: data-driven control, model-free adaptive 

control, adaptive/learning control, and intelligent 

transportation systems. 

References 

[1] Y. Shirai and Y. Ishihara, “Teito rapid transit authority’s automatic 

train operation,” Proc. IEEE, vol. 56, no. 4, pp. 605–615, 1968. 

[2] Available: http://en.wikipedia.org/wiki/Tokaido_Shinkansen 

[3] Available: http://en.wikipedia.org/wiki/Land_speed_record_for_ 

railed_vehicles 

[4] H.-W. Lawson, S. Wallin, B. Bryntse, and B. Friman, “Twenty years 

of safe train control in Sweden,” in Proc. IEEE Int. Conf. Computer Based 

Systems, 2001, pp. 289–293. 

[5] M. Matsumoto, S. Kitamura, and M. Sato, “High assurance technologies 

for autonomous decentralized train control system,” in Proc. IEEE 

Int. Symp. High Assurance Systems Engineering, 2001, pp. 220–229. 

[6] C. Binard and M.-V. Liefferinge, “ATBL—A fi rst step towards an European 

vital computer (EVC) for ATC,” in Proc. IEEE Electric Railways in 

a United Europe, Mar. 1995, pp. 116–120. 

[7] S. Yasunobu and T. Hasegawa, “Predictive fuzzy control and its application 

for automatic container crane operation system,” in Preprints 

of Second IFSA Congr., Tokyo, July 1987. 

[8] M.-K. Banerjee and N.-E. Hoda, “Review of the automatic train control 

system for Cairo Metro Line 2,” Power Eng. J., pp. 217–228, Oct. 

1998. 

[9] R.-D. Pascoe and N.-T. Eichorn, “What is communication-based train 

control,” IEEE Veh. Technol. Mag., pp. 16–21, Dec. 2009. 

[10] V.-A. Minin, V.-A. Shishliakov, J.-N. Holyoak, D.-A. Johnston, and 

N.-A. Lepsky, “Development of the communications-based train control 

system for Moscow metro,” in Proc. ASME/IEEE Railroad Conf., 1997, pp. 

201–210. 

[11] M. Heddebaut, “Leaky waveguide for train-to-wayside communication 

based train control,” IEEE Trans. Veh. Technol., vol. 58, no. 3, pp. 

1068–1076, Mar. 2009. 

[12] M. Miyachi, “The new ATC system using the digital transmission for 

super high-speed operation on the Shinkansen Lines,” in Proc. Int. Conf. 

Developments in Mass Transit Systems, 1998, pp. 68–73. 

[13] M. Miyachi, K. Matsumoto, and T. Matsuki, “The new generation of 

ATC for super high-speed operation on Shinkansen Lines,” in Proc. IEEE 

Vehicular Technology Conf., 1996, vol. 3, pp. 1613–1617. 

[14] S. Yasunobu, S. Miyamoto, and H. Ihara, “Fuzzy control for automatic 

train operation system,” in Proc. Int. Conf. Transportation Systems, 

1983, pp. 33–39. 

[15] T. Tang and L.-Y. Huang, “A survey of control algorithm for automatic 

train operation,” China J. Railway Soc., vol. 25, no. 2, pp. 

98–102, 2003. 

[16] S. Yasunobu and T. Hasegawa, “Evaluation of an automatic container 

crane operation system based on predictive fuzzy control,” Control 

Theory Adv. Technol., vol. 2, no. 3, pp. 419–432, Sept. 1986. 

[17] R.-M. Goodall and W. Kortum, “Mechatronic developments for railway 

vehicles of the future,” Control Eng. Pract., pp. 887–898, Oct. 2002. 

[18] C.-S. Chang and S.-S. Sim, “Optimising train movements through 

coast control using genetic algorithms,” in Proc. IEEE Electric Power Applications, 

1997, vol. 144, pp. 65–73. 

[19] S.-H. Han, Y.-S. Byen, J.-H. Baek, T.-K. An, S.-G. Lee, and H.-J. Park, 

“An optimal automatic train operation (ATO) control using genetic 

algorithms,” in Proc. IEEE Region 10 Conf. TENCON, 1999, vol. 1, pp. 

360–362. 

[20] L. Jia, X. Zhang, and Z. Xie, “Automatic train control: an intelligent 

approach,” in Proc. IEEE Computer, Communication, Control Power Engineering, 

1993, vol. 4, pp. 338–342. 

[21] Y. Huang and S. Yasunobu, “A practical design method of fuzzy 

controller based on control surface,” J. Jpn. Soc. Fuzzy Theory Syst., 

vol. 11, no. 5, pp. 841–847, 1999. 

[22] S. Yasunobu, S. Saitou, and Y. Suryana, “Intelligent vehicle control 

in narrow area based on human control strategy,” in Proc. World Multi 

Conf. Systemics Cybernetics and Informatics, 2000, vol. 7, pp. 309–314. 

[23] S. Sekine, N. Imasaki, and T. Endo, “Application of fuzzy neural 

network control to automatic train operation and tuning of its control 

rules,” in Proc. IEEE Int. Conf. Fuzzy Systems, 1995, vol. 4, pp. 1741–1746. 

[24] S. Sekine and M. Nishimurat, “Application of fuzzy neural network 

control to automatic train operation,” in Proc. IEEE Int. Conf. Fuzzy Systems, 

1995, vol. 5, pp. 39–40. 

[25] S.-P. Gordon and D.-G. Lehrer, “Coordinated train control and energy 

management control strategies,” in Proc. ASME/IEEE Railroad Conf., 

1998, pp. 165–176. 

[26] Y. Wang, Z.-S. Hou, and X.-Y. Li, “A novel automatic train operation 

algorithm based on iterative learning control theory,” in Proc. IEEE 

Conf. Service Operations, Logistics and Informatics, 2008, pp. 1766–1770. 

[27] J. Zhang, L. Jia, and X. Zhang, “On a novel fuzzy predictive control,” 

in Proc. American Control Conf., 1997, vol. 2, pp. 1251–1255. 


[28] B. Gao, H.-R. Dong, B. Ning, and Y.-X. Zhang, “Speed adjustment 

brake of automatic train operation system based on fuzzy-PID switching 

control,” in Proc. 6th Int. Conf. Fuzzy Systems and Knowledge Discovery, 

China, 2009, pp. 14–16. 

[29] W. Shang-guan, B.-G. Cai, J. Wang, and C.-X. Gou, “Research of 

MRM-based CTCS-3 level train operation control system simulation 

support technology,” in Proc. Asia-Pacific Conf., Shenzhen, China, 2009, 

pp. 418–421. 

[30] M. Matsumoto, A. Hosokawa, S. Kitamura, D. Watanabe, and A. 

Kawabata, “The new ATC system with an autonomous speed control 

with on-board equipment,” in Proc. Autonomous Decentralized Systems, 

2001, pp. 235–238. 

[31] B. Ning, K.-P. Li, and Z.-Y. Gao, “Modeling fi xed-block railway signaling 

system using cellular automata model,” Int. J. Mod. Phys. C, vol. 16, 

no. 11, pp. 1793–1801, 2005. 

[32] P.-F. Dressen, “Performance of frame synchronization in automatic 

train control communication system,” in Proc. Vehicular Technology 

Conf., 1989, vol. 2, pp. 517–522. 

[33] B. Ning, “Train-following model and traffi c fl ow feature in rail traffi c 

system,” Ph.D. thesis, 2005. 

[34] W. Shang-guan, B.-G. Cai, J. Wang, Y. Wang, and C.-X. Gou, “Research 

of system modeling and verifi cation method combine with UML formalization 

analysis and colored petri net,” in Proc. Int. Symp. Intelligent Information 

Technology Application, NanChang, China, 2009, pp. 488–491. 

[35] B. Ning, T. Tang, Z.-Y. Gao, F. Yan, F.-Y. Wang, and D. Zeng, “Intelligent 

railway systems in China,” IEEE Intell. Syst., vol. 21, no. 5, pp. 80–83, 

2006. 

[36] S.-G. Zhang, Design Method Studies for High-Speed Trains (in Chinese). 

Beijing, China: Railway Publishing House, 2009. 

[37] S.-G. Zhang, Optimization of the Beijing-Shanghai High-Speed 

Rail System (in Chinese). Beijing, China: China Railway Publishing 

House, 2009. 

[38] Manual for Railway Engineering (Fixed Properties), Amer. Railway 

Eng. Assoc., Chicago, IL, 16-2-2, 1970. 

[39] Z.-Z. Rao, Train Performance Calculation (in Chinese). Beijing, China: 

Railway Publishing House, 2005. 

[40] E.-C. Schmidt, “Freight train resistance, its relation to average car 

weight,” Univ. Eng. Exp. Station Bull., vol. 43, 1910. 

[41] E.-C. Schmidt and H.-H. Dunn, “Passenger train resistance,” Univ. 

Illinois Engine Exp. Station Bull., vol. 110, 1916. 

[42] W.-J. Davis, “Tractive resistance of electric locomotives and cars,” 

Gen. Electr. Rev., vol. 29, pp. 685–708. 1926. 

[43] W.-W. Hay, Railroad Engineering. New York: Wiley, 1982. 

[44] K. Sasaki and Y. Suda, “A study on the new wheel and rail tangential 

force model for the high-speed railway vehicles,” Veh. Syst. 

Dynamics, vol. 46 (Suppl.), pp. 737–750, 2008. 

[45] P.-E. Orukpe, X. Zheng, I.-M. Jaimoukha, A.-C. Zolatas, and R.-M. 

Goodall, “Model predictive control based on mixed H 2/H-infi nity control 

approach for active vibration control of railway vehicles,” Veh. Syst. 


[46] J. Zhou, G. Shen, H. Zhang, and L. Ren, “Application of modal parameters 

on ride quality improvement of railway vehicles,” Veh. Syst. 


[47] G. Charles, R. Goodall, and R. Dixon, “Model-based condition monitoring 

at the wheel-rail interface,” Veh. Syst. Dynamics, vol. 46 (Suppl.), 

pp. 415–430, 2008. 

[48] H. Liu and J. Zheng, “Study on ride comfort of train system,” China 

Railway Sci., vol. 25, no. 5. pp. 20–25, Oct. 2004. 

[49] H. Meng, W. Zhai, and K. Wang, “Infl uence of the second suspension 

for locomotive dynamic,” Railway Locomotive Car, vol. 25, no. 5, 

pp. 1–4, Oct. 2005. 

[50] R. Luo, J. Zeng, and H. Dai, “Modelling and ride quality analysis 

of railway train system,” China Railway Sci., vol. 27, no. 1, pp. 72–77, 

Jan. 2006. 

[51] A.-C. Zolotas, J. Wang, and R.-M. Goodall, “Reduced-order robust 

tilt control desigh for high-speed railway vehicles,” Veh. Syst. Dynamics, 

vol. 46, pp. 995–1011, 2008. 

[52] J. Carballeira, L. Baeza, A. Rovira, and E. Carcia, “Technical Characteristics 

and dynamics modelling of Talgo trains,” Veh. Syst. Dynamics, 

vol. 46 (Suppl.), pp. 301–316, 2008. 

[53] H. Zamzuri, A.-C. Zolatas, and R.-M. Goodall, “Tilt control design 

for high-speed trains: A study on multi-objective tuning approaches,” 

Veh. Syst. Dynamics, vol. 46 (Suppl.), pp. 535–547, 2008. 

[54] Chinese Transportation Bureau, “Commercial dedicated lines,” (in 

Chinese), Rep. 060606PPT. 

[55] H. Oshima, S. Yasunobu, and S. Sekino, “Automatic train operation 

system based on predictive fuzzy control,” in Proc. Int. Workshop Artificial 

Intelligence for Industrial Application, 1988, pp. 485–489. 

[56] S. Yasunobu, “Automatic container crane operation based on a predictive 

fuzzy control,” Trans. Soc. Instrum. Contr. Eng., vol. 14, no. 6, pp. 

739–744, 1978. 

[57] X.-W. Sun and Y.-S. Chen “The simulation of automatic train operation 

based on fuzzy prediction control strategy,” (in Chinese), Comput. 

Eng. Applicat., vol. 5, pp. 214–217, 2002. 

[58] J. Schutte, “Recent trends in automatic train controls,” in Proc. Intelligent 

Transportation Systems, Oakland, CA, 2001, pp. 813–819. 

[59] Y. Wang and Z. Hou, “Terminal iterative learning control for station 

stop control of a train,” in Proc. Symp. Learning Control at IEEE CDC, 

2009. 

[60] J. Zhang, L. Jia, and X. Zhang, “A novel fuzzy predictive control 

for automatic train operation process,” China Railway Sci., vol. 4, pp. 

101–109, 1996. 

[61] B.-G. Cai, W. Shang-guan, X.-Q. Li, and J. Wang, “Research on supporting 

technology for simulation CTCS-3 based on multi-resolution 

modeling,” J. Beijing Jiaotong Univ., vol. 34, pp. 5–10, 2010. 

[62] E. Nishinaga, J.-A. Evans, and G.-L. Mayhew, “Wireless advanced 

automatic train control,” in Proc. ASME/IEEE Railroad Conf., 1994, pp. 

31–46. 

[63] V.-E. Kozura, V.-A. Nepomniaschy, and R.-M. Novikov, “Verifi cation 

of distributed systems modelled by high-level Petri nets,” in Proc. Parallel 

Computing in Electrical Engineering, 2002, pp. 61–66. 

[64] K.-M. Junaid and S. Wang, “Automatic cruise control modeling a 

lattice PWL approximation approach,” in Proc. Intelligent Transportation 

Systems, 2006, pp. 1370–1375. 

[65] Design/CPN [Online]. Available: http://www.daimi.au.dk/ 

designCPN/ 

[66] L. Jansen, M. Meyer, Z. Horste, and E. Schnieder, “Technical issues 

in modelling the European train control system,” in Proc. 1st CPN Workshop, 

DAIMI PB 532, Aarhus Univ., 1998, pp. 103–115. 

[67] G. Decknatel, “Modelling train movement with hybrid Petri nets,” 

in Proc. FME Rail Workshop, Stockholm, 1999, vol. 99, pp. 11–12. 

[68] M. Meyer, Z. Horste, and E. Schnieder, “Modeling train control 

system with petri nets—A functional reference architecture,” in 

Proc. IEEE Int. Conf. Systems, Man and Cybernetics, 2000, pp. 3081– 

3086. 

[69] D. Wu and Y. Zhang, “Researching colored Petri Nets model of 

communication based train control system,” J. Syst. Simul., vol. 17, pp. 

2388–2391, 2005. 

[70] L.-K. Siu and C.-J. Goodman, “An object-oriented concept for simulation,” 

in Proc. COMPRAIL Int. Conf., Washington, DC, 1992. 

[71] L.-K. Siu, “An object-oriented railway system and power network 

simulator,” Ph.D. thesis, Univ. Birmingham, 1995. 

[72] F.-M. Rachel and P.-S. Cuganasca, “Objected-oriented approach 

for automatic train operation control systems,” in Proc. Computers in 

Railways IX. Wit Press, 2004. 

[73] Y. Lu and T. Tang, “The model based design of automatic train 

operation simulation system,” J. China Railway Soc., Dec. 2001, pp. 

50–54. 

[74] I. Pataricza, G. Majzik, and G. Huszerl, “VárnaUML based design 

and formal analysis of a safety,” in Proc. Critical Railway Control Software 

Module (Formal Methods for Railway Operation and Control Systems 

of Symp.), G. Tarnai and E. Schnieder, Eds. Budapest, Hungary: L 

Harmattan, May 15–16, 2003, pp. 125–132. 

[75] National Research Council, Technology for the United States Navy 

and Marine Corps. 2000–2035 Becoming a 21st Century, vol. 9. Force National 

Academy Press, 1997. 

[76] S. Sekine, M. Kanou, M. Ogata, and A. Higashide, “Advanced technique 

for MRM (multi-resolution models),” in Proc. Spring Simulation Interoperability 

Workshop, 2001. 

[77] B. Cai, “Some new research on system modeling and verifi cation 

method for CTCS level-3,” NSFC Report, Beijing, 2009. 

[78] F. Y. Wang and S. Tang, “Artifi cial societies for integrated and sustainable 

development of metropolitan systems,” IEEE Intell. Syst., vol. 

19, no. 4, pp. 82–87, 2004. 

[79] F. Y. Wang, H. Zhang, and D. Liu, “Adaptive dynamic programming: 

An introduction,” IEEE Comput. Intell. Mag., vol. 4, no. 2, pp. 39–47, 2009.

Automatic Train Control System Development and Simulation for ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?