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Abstract

One of the main goals of the railway simulation technique is the formation of a model that can be easily tested for any desired changes and modifications in infrastructure, control system, or in train operations in order to improve the network operation and its productivity. RailSys3.0 is a German railway simulation program that deals with this goal. In this paper, a railway network operation, with different suggested modifications in infrastructure, rolling stocks, and control system, using RailSys3.0, has been studied, optimized, and evaluated. The proposed simulation program (RailSys 3.0) was applied on ABO-KIR railway line in Alexandria city, as a case study, to assess the impact of changing track configuration, operating and control systems on the performance measures, time-table, track capacity and productivity. Simulation input, such as track element, train and operation components of the ABO-KIR railway line, has been entered to the computer program to construct the simulation model. The simulation process has been carried out for the existing operation system to construct a graphical model of the case-study track including line alignment and train movements, as well as to evaluate the existing operation system. To improve the operation system of the railway line, eight different innovative alternatives are generated, analyzed and evaluated. Finally, different track measures to improve the operation system of the ABO-KIR railway line have been introduced.

Keywords

Railway operation; Simulation technique; Automatic train control; Moving block system; RailSys3.0; Track capacity

1. Introduction

The improvement of railway operation has long been a major objective of a wide range of transportation studies. One of the most difficult problems in studying the enhancement of a railway line is the large quantities of data required and calculations needed.

Simulation is a process whereby any phenomenon or system with similarities can be transposed and represented by a simpler or less complex model. Simulation models try to move from rigid mathematical formulations without neglecting logical evaluation.

Computer simulation is especially valuable for railroad planning. Once developed and calibrated; models can be used to compare the benefits, impacts, and costs of various different improvement packages. To analyze more than a few improvement packages by hand would be prohibitively time consuming. Thus effective railroad simulation models enable planners to identify and evaluate more alternatives ultimately leading to more creative and comprehensive problem solutions.

The purposes of this paper was to study, and evaluate different suggested modifications in infrastructure, rolling stocks, and control system in a railway network operation, namely ABO-KIR railway line in Alexandria City. Railway Simulation Computer Program RailSYS3.0 has been utilized for this purpose.

This paper is divided into four major sections. The first section identifies the main categories of the railway simulation techniques. The second section presents and analyzes the computer simulation programs in railway systems. In the third section the RailSys railway simulation program has been described. The forth section introduces an application of RailSys Simulation Program on ABO-KIR railway line. This application has been studied through the analysis of the existing situation and introducing different innovation scenarios to improve the rail line.

2. Railway simulation techniques

Railway Simulation techniques can be divided into the following categories:

• Track Infrastructure Simulation (TIS),
• Train Performance Simulation (TPS), and
• Railway Operation Optimization (ROO).

In the Track Infrastructure Simulation (TIS), the infrastructure can be modeled and visualized more precisely, and the effect of changing both track configuration and operating systems can be examined over a range of traffic predictions, in order to evaluate methods and timing of alternatives [1]. TIS has been used in different researches to:

• Support the design and optimization of infrastructure process – program VILLON [1].
• Support the microscopic modeling of various types of transportation logistic terminals containing railway and road infrastructure, such as airports marshaling yards and railway passenger stations- program VILLON [1].
• Support the interlocking system [2].
• Support to study the effect of the railway infrastructure change in a railway cargo network- program ARENA [3].

In the Train Performance Simulation (TPS), the computer models can be used to investigate the impacts of modifying track facilities or operating rules on the train performance.

TPS has been used in different researches to:

• Simulate the operation of a single train over a specified railway route, in the case of changing train performance parameters, e.g. the maximum speed, but it does not model the interaction between several trains on a railway line [4].
• Assess power network design and operation on mass transit railways [5].
• Estimate train operating cost [6].
• Determine scheduled operating time of train.
• Determine minimum speed on ruling grade.
• Determine the effect of changing the time table.
• Study the effect of changing speed restriction or station stop [6].
• Estimate the fuel consumptions and instantaneous [7].

Railway Operation Optimization (ROO) models can be used to utilize the simulation techniques in optimizing some criteria, such as scheduling and travel costs ROO has been used in different researches for:

• Improvement of operation of railway network.
• Education [8].
• Schedule of trains [8].
• Designing two-level computer – aided railway control systems [9].
• Analyzing human error in railway operation,
• Optimization of energy consumption for rail public transit system [10]
• Estimating delays and delays Propagation [11], and
• Evaluating different railroad improvement strategies.

3. Computer simulation programs in railway systems

Railway simulation programs can be categorized as follows:

• Macroscopic simulation programs and
• Microscopic simulation programs.

The basis of the macroscopic simulation programs is the average movement of a group of trains. These programs model the infrastructure in less detail than the microscopic one. They provide improved computational performance but reduced detail of representation. Macroscopic models use average or other statistical data to evaluate operation of the transportation system; they do not model individual unit (e.g. train) operations nor do they consider how trains are impacted by other trains or how safety systems impact train performance.

A common type of the macroscopic model is the Net Evaluation Model (NEMO). The macroscopic simulation model NEMO is a strategic planning tool for evaluation of infrastructure and operational measures in railway systems [12] and [13].

Microscopic models are based on current timetables, detailed infrastructure data and are typically computationally intensive but accurate in representing train movements. Microscopic models consider the impact of trains on each other when they simulate train operations by modeling the operation of each individual train during a user-defined time step (often one second) and then repeating the process for the entire simulation period.

There are two types of microscopic simulation models; Synchronous microscopic programs, and Asynchronous microscopic programs. Synchronous microscopic programs simulate all train operations in a single model run, while asynchronous microscopic programs simulate operations in a series of model runs.

4. RailSys simulation program

RailSys simulation program is a synchronous microscopic simulation program for railway systems that made at the Institute of Transport, Railway Construction and Operation (IVE). RailSys consists of the four program elements (Fig. 1), these are:

• Infrastructure Manager,
• Timetable Manager,
• Simulation Manager, and
• Evaluation Manager

Figure 1. The structure of RailSys simulation program.

The program begins to create a new project with the “Infrastructure Manager”. In this module the infrastructure data can be entered, which means managing of tracks, signals and all further relevant elements. All data is provided with an accuracy of 1 m. For mapping the protection system (signal and control systems), all important German protection systems have been firmly installed. Moreover, Multiple-Aspect Block Signaling and Moving Block, operation with absolute braking distance, by which also running at sight can be mapped. The infrastructure data can be managed in variants within a project. This enables to keep an initial network and planned variants in one project, in order to test later the effects of the infrastructure changes on the timetable.

If a track network is available, an infrastructure variant, project editing with the “Timetable and Simulation Manager” can be performed. This module is the largest part of RailSys simulation program. It consists of the two parts “timetabling and simulating”, both are available in one program.

By means of the “Simulation Manager” individual operational days can be simulated. If the nominal timetable still contains conflicts, for example, the actual delays caused by braking and restarting at red signals become visible. Temporary track blocking is also possible. In the simulation, the timetable can be simulated with rerouted trains. In order to test the stability of a nominal timetable, a large number of operational days can be provided with random disruptions. In multiple-simulation, trains are then guided through the network by a dispatching function according to their user-defined priority. In this way, the delay to be expected for any train group can be determined on the basis of the nominal timetable used in simulation.

The last module is the “Evaluation Manager”, which is purely an evaluation program for the data of the simulation process. This module enables statistical evaluation of various delay types in the form of diagrams. Evaluation can be represented in this module by graph, statistics, or tabular forms.

5. Application of RailSys simulation program on ABO-KIR railway line

RailSys simulation program has been applied on ABO-KIR regional rail system as a case study. The main objective of this application is to assess the impact of changing track configuration, operating and control systems on the performance measures, time-table, track capacity and productivity.

5.1. Analysis of the existing situation of the ABO-KIR railway regional line

Abo-Kir railway line is a regional rail line that covers 3% of the total daily trips done in Alexandria city. The urban transport demand in Alexandria city is covered depending on the following transport mode:

• Group Taxi, with 25% of the transport demand,
• Taxi, with 27% of the transport demand,
• Private car, with 18% of the transport demand,
• Tram (street car and LRT), with 13% of the transport demand,
• Bus (public and private), with 10% of the transport demand,
• Minibus (public) with 4% of the transport demand, and
• ABO-KIR rail line with 3% of the transport demand.

ABO-KIR railway line is a double track regional system and considered as the fastest mean of local transport in the city of Alexandria. It starts from Alexandria main station and travels along the southern urban parts of the city to ABO-KIR station. The line is 22.11 km long and the average commercial speed is about 26.53 km/hr and has 16 stops (14 intermediate). The distance between stops varies between 500 m and 2800 m. Fig. 2 indicates an overview of ABO-KIR railway line with stations locations. Stations are introduced by numbers, starting from Alexandria station as number 1 to Abo Kir station as number 16. Maximum grade of ABO-KIR line is 4 per thousand, and the minimum radius of horizontal curve is 750 m.

Figure 2. Overview of ABO-KIR Line.

The track is ballasted, the rail is Vignol 52, and the sleeper is wooden type. The control system applied is the Electro-Mechanical Relay Technology (EMRT), in which track circuits are installed along the line to detect the presence of trains. The signal system applied is the electrical system with two-signal aspects. Switches along the line are mechanical.

The trains running on ABO-KIR line are blocked composition trains. Each set consists of a diesel-electric locomotive at the head of the train, and six carriages. There are a total of 12 sets of trains, from which 11 in service and one as stand-by at ABO-KIR.

The locomotives characteristics of the existing rolling stock can be summarized as follows:

• Model Designation: G22W/AC
• Locomotive Type: (BB) 0440
• Locomotive Horsepower: 1650–1500
• Full Engine Speed: 900 RPM
• Traction Motors: Model D77
• Air Compressors (with 26L Air Brake): Model WBO
• No. of Cylinders: 3
• Cooling: Water
• Truck Model: G.B.

Brake Rigging: Single Shoe

• Shoes: Cast Iron
• Height over Horn: 410.5 cm.
• Width over under frame: 274.3 cm.
• Fuel Capacity (basic): 3000 L.

Existing schedule timetable is operating by 176 trains scheduled per day, from which 88 serve the route in each direction. In the peak hours the trains run at intervals of 10 min, extended to 15–30 min at off-peak hours. The trains have a theoretical capacity of 1500 passengers per train; more than 132,000 passengers could be carried daily in each direction. According to the official timetable, trains cover the line including stopping time at the intermediate stations in 50 min (stopping time at the intermediate stations is one minute) and the average existing commercial speed is about 26.53 km/h.

Analysis of the operation of the ABO-KIR rail line indicates that large delays are noticed in arrival and departure times of the trains. Some of these delays exceeded 60 min per train especially at Alexandria main station. Some of scheduled trips may be canceled due to these delays. Investigating the reasons causing these delays, it has been found that the existence of the intersection of Cairo/Alexandria rail line between intercity main tracks (on the right side of ABO-KIR line) and the shunting yard (on the left side of ABO-KIR line) is the major source of these delays. Furthermore, exceeding train dwell times and various maintenance operations for tracks and trains and signaling system are auxiliary reasons for the train delays.

5.2. Innovation scenarios for operation improvements of ABO-KIR line

To improve the operation of the ABO-KIR rail line nine different scenarios have been suggested and evaluated, and these are as follows:

• Do Nothing Scenario (basic scenario),
• ATC (Automatic Train Control) Scenario.
• ATC and High speed Scenario
• Moving Block Scenario,
• Moving Block and High speed Scenario,
• Moving Block and recent passenger information system scenario
• Moving Block, high speed, and recent passenger information system Scenario
• Moving Block and mixed train operation (regular and quick trains) Scenario, and
• Moving Block, High speed, and mixed train operation Scenario.

Applying the RailSys simulation program on the innovation scenarios indicates the following facts:

• For DO Nothing Scenario, the trip time is 44.38 min, with delay per train of 5.62 min. The maximum speed on the longest section in this scenario (between Alexandria station and El Hadara station) reached 80 km/h, and on the shortest section, (between Ghebrial station and El Raml station) reached 53 km/h, and the commercial speed of the whole line is 29.89 km/h.
• For ATC Scenario, the simulation resulted that the trip time decreased slightly to 42.75 min and the maximum running speed reached 80 km/h, and the commercial speed increased slightly to 31.03 km/h.
• For ATC and High speed Scenario, the simulation indicated that trip time is slightly decreased to 41.33 min and the maximum running speed reached 83–88 km/h on the longest sections. The commercial speed increased to 32.10 km/h. The simulation of this alternative showed that a high speed of 120 km/h cannot be achieved even on long block sections under the existing operating conditions.
• For Moving Block Scenario, the simulation derived that the trip time is slightly decreased to 41.75 min and a maximum running speed of 83–88 km/h was reached on the longest sections. The commercial speed was increased to 31.77 km/h.
• For Moving Block and High speed Scenario, the simulation illustrates that the trip time is decreased to 40.38 min and the maximum running speed reached 88 km/h on the longest sections. The commercial speed was increased to 32.86 km/h. The simulation of this alternative showed that a high speed of 120 km/h cannot be achieved even on long block sections under the existing operating conditions.
• For Moving Block and recent passenger information system scenario, the simulation resulted that the trip time decreased to 35.83 min and the maximum running speed reached 80 km/h on the longest sections, and the commercial speed was increased to 37.02 km/h.
• For Moving Block, high speed, and recent passenger information Scenario, the simulation showed that the trip time decreased to 34.75 min, the maximum running speed increased to 88 km/h on the longest sections, and the commercial speed increased to 38.18 km/h. The simulation of this alternative showed that a high speed of 120 km/h cannot be achieved even on long block sections under the existing operating conditions.
• For Moving Block and mixed train operation Scenario, the simulation indicated that the trip time decreased to 35.83 min for the regular trips and to 25 min for the quick trips, and the maximum running speed reached 80 km/h on the longest sections for both trip types. The commercial speed increased to 38.18 km/h for the regular trips and to 53.06 km/h for the quick trips.
• For Moving Block, High speed, and mixed train operation Scenario (last scenario), the simulation derived that the trip time decreased to 34.75 min for the regular trips and to 22.37 min for the quick trips, and the maximum running speed reached 88 km/h on the longest sections for regular trip type and to 92 km/h for quick trip type. The commercial speed increased to 38.18 km/h for the regular trips, and to 59.31 km/h for the quick trips.

Table 1 illustrates a comparison between the innovation scenarios. Appendix A illustrates samples of the output of simulation program for the innovation scenarios.

Table 1. Comparison between Innovation scenarios of ABO-KIR rail line.

Scenario	Trip type	Block section type	Trip time (min)	Dwell time (s)	Max. allowable speed (km/h)	Max. running speed (km/h)	Commercial speed (km/h)	Improvement percentage (%)
								In trip time	In Com. speed
Scenario-1	Regular	Fixed	42.75	60	80	80	31.03	14.5	17.0
Scenario-2	Regular	Fixed	41.33	60	120	88	32.10	17.3	21.0
Scenario-3	Regular	Moving	41.75	60	80	80	31.77	16.5	19.8
Scenario-4	Regular	Moving	40.38	60	120	88	32.86	19.2	23.9
Scenario-5	Regular	Moving	35.83	30	80	80	37.02	28.3	39.5
Scenario-6	Regular	Moving	34.75	30	120	88	38.18	30.5	43.9
Scenario-7	Regular	Moving	35.83	30	80	80	37.02	28.3	39.5
	Quick	Moving	25.00	30	80	80	53.06	50.0	100.0
Scenario-8	Regular	Moving	34.75	30	120	88	38.18	30.5	43.9
	Quick	Moving	22.37	30	120	92	59.31	55.3	123.6

6. Conclusions

This paper has focused on computer application in railway operation. The categories of the railway simulation technique have been identified. Computer simulation programs in railway systems have been analyzed. Application of the German RailSys railway simulation program on ABO-KIR rail line (in Alexandria city) has been introduced. Different innovation scenarios for improvement of the railway line have been simulated, analyzed and evaluated. Applying the simulation model on the case study derived the following facts:

• Application of ATC (Automatic Train Control) for the current operation system on ABO-KIR railway line will reduce the current travel time by 15%.
• Increasing the train speed of ABO-KIR railway line, by updating locomotives, and application of ATC will reduce the current travel time by 17%.
• Introducing moving block system with the current rolling stock (without increasing trains speed) will reduce the current travel time by 17%.
• Introducing moving block system and increasing the train speed (by updating the rolling stock) will reduce the current travel time by 19%.
• Application of moving block system with updating the wagons and introducing passenger information system will reduce the current travel time by 28%.
• Application of moving block system, updating the wagons, increasing train speed and introducing passenger information system will reduce the current travel time by 31%.
• Application of moving block system, updating the wagons, increasing train speed, introducing passenger information system, and application of mixed operation systems (quick and regular trains) will reduce the current travel time by about 50% for quick trains and 28% of regular trains.
• Application of moving block system, updating the wagons, increasing train speed, introducing passenger information system, increasing train speed, and application of mixed operation systems (quick and regular trains) will reduce the current travel time by about 55% for quick trains and 31% of regular trains.
• A high speed of 120 km/h cannot be achieved even over long block sections under the existing operating condition because of the short intervals between stations.
• Regardless of the type of the used train control system, the maximum running speed resulted from the simulation is 88.0 km/hr for regular trips.
• The minimum trip time obtained from different simulation runs is 34.75 min for regular and 22.37 for quick trains (scenario-8). This alternative considers a 30 s as a dwell time at each intermediate station, and also requires innovation in car and platform information systems such as number and type of doors (automatic) in each carriage, existence of Central Camera TV Monitor in trains, and existence of automatic passenger information systems on platform. This Alternative also requires the introduction of moving train operation system and mixed operation system (quick and regular system).
• In the case of introducing a mixed train operation (regular and quick trips), the values of the operating measures are better than those of the other alternatives.
• Finally, improving the trip time of the rail line can reach from 30% to 55% (scenario 8) through improving strategies suggested for the case study. This means an improvement of the productivity of the rail line with the same percent can be reached. The question is the financing of such improvements. The Decision maker has to evaluate the financing required for such improvements and the gaining productivity of the rail line and its effects on the modification of the transport demand in the city and also its effect on solving the traffic problem on the road network.

Acknowledgment

The Authors thank the Institute of Transport, Railway Construction and Operation (IVE), Hannover University (Germany) for giving us the possibilities to use RailSys. 3 for research purposes.

Appendix A

Samples of the Output of Simulation program for the Innovation Scenarios

Timetable diagram of ABO-KIR railway line for Alternative (Scenario-5).

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