Evaluation of ATO Benefits under Real-Time Rail Traffic Control on Mainlines

Annik Jeiziner Institute for Transport Planning and Systems Swiss Federal Institute of Technology, ETH

Zurich, Switzerland jannik@student.ethz.ch

Abstract—This work investigates the combination of a traffic management system (TMS) and automatic train operation (ATO) on mixed traffic systems. The main questions are, how the distribution of ATO on different train types influences the traffic system and how sharing knowledge about train performance reductions between the systems improves the conflict solving. This is done by setting up a simulation framework and applying it to a test case from the Dutch Railways. The evaluation shows that applying ATO saves energy, especially on regional trains, and increases the compliance to the given timetable, especially for long-distance trains. Further, it becomes evident an ATO, which is informed about the performance reductions, has no influence on the traffic. An informed TMS might improve the traffic state, however this remains to be investigated in further detail.

Keywords—automatic train operation, traffic management system, open loop control, mainlines, mixed rail traffic, simulations

I. INTRODUCTION In general, railway operation can be divided into two

control loops as shown in Fig. 1 [1]. The outer control loop represents the railway traffic control, while the inner control loop is the train operation. Today, the state-of-the-art execution of both control loops on mainlines happens manually. The railway traffic control is done by a dispatcher, who monitors the traffic state, detects conflicts and reorders the traffic by sending new schedules to the train operation. The train operation is executed by a train driver, who controls the speed and acceleration of the train according to the given timetable and additional information given by fixed signals. Only very few automated processes are implemented to guarantee a safe train operation. One such example is the Automatic Train Protection (ATP, e.g. ETCS) supporting the train's overspeed protection and keeping a safe headway between trains.

However, the increasing need in sustainable transportation of people and goods has led to a growing density of traffic on a limited railway network. Extending the network with new railway lines is rarely an option, as such projects require huge investments. Therefore, the capacity of the existing network needs to be increased while simultaneously keeping the energy consumption low. This can be realized by further automating processes, for example with a traffic management system (TMS) in railway traffic control or with automatic

train operation (ATO) in train operation. A TMS automatically detects conflicts in real-time, solves them centrally with all available traffic information by reordering, retiming or rerouting trains and communicating the new schedule to the trains. An ATO makes real-time decisions on the train's acceleration, braking, cruising and coasting commands, while it tries to reduce the energy consumption of the train by precisely controlling it to drive in the most efficient way possible.

Extensive research has been conducted separately on both control loops for several years and the advantages are promising. However, few have investigated the combination of the two systems, which is called integrated real-time rescheduling on mainlines [2]. Therefore, one aim of this work is to set up such an integrated framework as was proposed by [2] and thus to evaluate the benefits of ATO under real-time traffic control in terms of punctuality and energy efficiency. Further, as ATO is mainly investigated on metro lines, this work aims to find the optimal proportion and allocation of trains with ATO on a mixed traffic network.

II. LITERATURE REVIEW

A. Traffic Management System In general, railway traffic is based on a timetable, defining

the exact arrival and departure times for each train at each station. However, railway traffic is subject to many different kinds of perturbations propagating quickly through the system. Therefore, real-time dispatching is needed in order to restore the original timetable and solve conflicts. A conflict in that sense occurs, when two or more trains require the same piece of infrastructure at the same time. Nowadays, most of the railway traffic control is done by human dispatchers, which base their decisions on training and experience, lacking intelligent decision support. Nevertheless, the research on the topic of automated railway traffic control by a TMS is extensive. An overview can be found in [3] and [4]. There exist a variety of approaches in terms of time of application, knowledge about traffic state, level of detail or solving algorithm. The basic principle is to either reroute, retime or reorder trains in order restore the original timetable while fulfilling certain other objectives, such as the reduction of the total delay or the reduction of the consecutive delay. A key approach is the computation of train path envelopes (TPE), which allow each train to use a certain part of infrastructure (e.g. track) for a certain amount of time [5]. The TMS used in this work is based on [6].

B. Automatic Train Operation ATO is the automatic real-time control of a train’s

acceleration, braking, cruising and coasting commands according to the actual traffic plan. It is responsible for the execution of the rescheduled timetable elaborated by the outer control loop. The technology has emerged over the last few

Fig. 1. The outer and inner control loops of railway traffic control [2].

years with the development of control and computer technologies and is considered to be a very promising approach in order to improve energy efficiency and network capacity compared to the manual train control by a train driver. The latter is based on training and experience rather than exact computation, which is why it is difficult to guarantee the optimal operation, for example in terms of energy efficiency, punctuality and stopping accuracy. An overview of available research can be found in [7]. The ATO implemented in this work is based on [8].

C. Integrated traffic system Overall, the research on both, TMS and ATO is quite

mature. However, few consider the combination of the two systems as it was proposed first by [1], although this framework promises many advantages. On the one hand, a TMS has the clear overview over the entire traffic state and possible conflicts. Therefore, the TMS should not only reroute, retime and reorder trains, but it could also compute and transmit an optimal speed trajectory directly to the train. Such a framework spares the train from braking or stopping at signals due to an unexpected, rescheduled timetable and it can therefore prevent both time and energy loss. Reference [9] proposes such a model, where the dispatching and train trajectory computation are not done sequentially, but rather both tasks are part of the entire problem solving. Further, it has to be mentioned that a TMS nowadays predominantly has to deal with non-continuous train detection, depending on certain fix-installed infrastructure elements (e.g. axle counters). However, an ATO system could transmit not only a (quasi-)continuous train position and speed, but also other train dynamics, such as the train’s acceleration and deceleration capabilities [10]. This could improve the TMS prediction of future traffic states. Further, if the ATO could (quasi-)continuously receive messages, the updated schedules could be implemented immediately. A more holistic theoretical framework has been elaborated by [2].

D. Contribution of this work According to the background information and literature

review given above, this work aims in two directions. First, the integration of ATO under real-time traffic management on mainlines is examined. For this purpose, a simulation environment of a Dutch Railway corridor was created, on which both long-distance trains and regional trains are operated. The simulation was equipped with an open loop TMS and ATO. To find the optimal penetration and distribution of trains with ATO, different configurations were tested over several delay scenarios. Second, this work aims to investigate the impact of sharing knowledge about reduced train performance on the traffic system. More specifically, the same framework as mentioned above was used, while the performance of several trains was reduced. All scenarios were run three times: First, neither the ATO nor the TMS had knowledge about the train performance reductions, in the second run-through, the ATO had this knowledge while in the third run-through both the ATO and the TMS had this knowledge. With this configuration, the importance of sharing knowledge on current train dynamics was investigated. In summary, this work aims to answer the following questions:

Q1 How does the penetration and distribution of ATO trains influence the traffic flow in mixed traffic systems?

Q2 How does sharing knowledge about train performance reductions influence the traffic flow?

III. METHOD The simulation framework implemented in this work is

shown in Fig. 2. It consists of three parts: the traffic control, the train operation and the railway system. On the one hand side, a TMS is responsible for the rescheduling of the original timetable according to some initial delays. It is assumed to be all knowing and is implemented as open loop. The travel time for each train is estimated from the original timetable, which includes some buffer times. Thus, the TMS estimates travel times larger than the technically minimum possible travel time. A more detailed explanation is available in [6]. On the other hand, the train operation consists of two parts. First an offline speed optimizer calculates several speed profile lookup tables with respect to the given rolling stock and infrastructure. Second, an online ATO algorithm decides on the optimal speed trajectory with regard to the current train speed, the speed profile lookup tables and the rescheduled timetable given by the TMS. The ATO prioritizes on-time arrival over energy reduction. The full description is available in [8]. Both the rescheduled timetable as well as the optimal action are transmitted to the microscopic simulation tool OpenTrack, representing the railway traffic system.

IV. CASE STUDY

A. Data Set As test case the corridor between Utrecht and ’s-

Hertogenbosch from the Dutch Railways has been used. The corridor is about 48 km long and has nine stations (see Fig. 3). To reduce complexity, gradients and radii of curves were not respected. The corridor is simulated in one direction only (Ut–Ht), however this does not affect the results as the other direction operates on different tracks. Two train types are operated on this corridor: Intercity trains (IC) and Sprinters (SP). The three IC courses drive directly from Utrecht to ’s-Hertogenbosch without any intermediate stop. The two SP courses stop at each station, one terminates in Geldermalsen, the other in ‘s-Hertogenbosch. For the simulation a time horizon of two hours was considered (8:00 – 10:00) with a half-hourly clocked timetable, resulting in 20 trains departing from Utrecht (12 ICs, 8 SPs).

Fig. 2. Simulation framework.

Fig. 3. Corridor Utrecht – ‘s-Hertogenbosch.

B. Scenarios To evaluate the proposed model, a Monte-Carlo

framework was set up, where 20 different delay cases with random initial delays were generated. The delays were drawn from a 3-parameter Weibull distribution, based on the experience with real life data in [11]. The parameters were set as follows:

IC: scale = 394, shape = 2.27, shift = 315;

SP: scale = 235, shape = 3.00, shift = 186.

The evaluation of research question Q1 was done by implementing eight ATO-scenarios (Table I). As benchmark served the scenario, in which no train is operated with ATO and the complementary scenario, in which all trains are operated with ATO. In between, the effect of ATO on train types was evaluated by operating 50 % or 100 % of all IC or SP with ATO. Two additional scenarios, in which 40 % or 80 % randomly selected trains were equipped with ATO, conclude the set of scenarios. All scenarios were simulated twice, once with rescheduling from the TMS and once without.

In order to answer Q2, eight performance scenarios were implemented (see Table II). As benchmark served the scenario, in which all trains have full performance, and the one, in which all trains have reduced performance. In order to imitate the realistic case where several trains of one train type have reduced performance due to some technical disturbance or weather conditions, six further scenarios were defined in which 25 %, 50 % or 100 % of all IC or SP, respectively, were reduced in performance. To answer the key question of Q2, all performance scenarios were simulated with different knowledge distributions. First, neither the TMS nor the ATO knew about the performance reductions ("no knowledge"). Second, the ATO was informed (“ATO knowledge”) and finally, both the ATO and the TMS were informed about the performance reductions ("ATO and TMS knowledge").

V. RESULTS The results are presented as follows: First, the key findings

regarding the implementation of ATO, TMS and their combination is discussed. Afterwards the extensive results of the simulations are presented.

A. Key Results 1) Implementation of ATO A train simulated in OpenTrack without ATO drives at

maximum acceleration, full speed and brakes at the last possible moment in order to stop at the station. Since the original timetable includes buffer times and is based on realistic travel times (including weather conditions, driver behavior, etc.), the IC with no ATO in Fig. 4 will arrive some minutes earlier than scheduled. If the IC however is equipped with ATO, it will not drive at full speed. Instead, the ATO will respect TPE points at each station and command the to train to coast such that it will arrive on-time at the following station. This is repeated for each TPE point, thus in this work for each station the train passes. Therefore, the train will comply much stricter to the original timetable as can be seen in the train path and not arrive early.

The situation looks rather similar for a SP. In Fig. 5 it can be seen, that a SP without ATO will accelerate to maximum speed between all stations and always arrive some seconds to minutes early. However, at each station it will wait for the departure time indicated in the original timetable. Therefore, a SP inherently follows the timetable much stricter. ATO on SPs will prevent acceleration to full speed between all stations and instead command the train to start coasting such that the train arrives on-time and minimizes its energy consumption.

Considering the energy consumption in Fig. 6, it becomes evident that the ATO can save a considerable amount of energy on both train types. An IC operated with ATO can save 57 kWh (22 %) compared to the case without ATO. A SP with

Fig. 4. Speed profiles and train paths of IC without and with ATO.

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TABLE I. Simulations regarding Q1 ATO scenarios with TMS no TMS ATO1: no ATO

All 20 delay cases

ATO2: all ATO ATO3: 50 % of IC ATO ATO4: 100 % of IC ATO ATO5: 50 % of SP ATO ATO6: 100 % of SP ATO ATO7: 40 % random ATO ATO8: 80 % random ATO

TABLE II. Simulations regarding Q2 Performance scenarios

no knowledge

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All 20 delay cases

25 % of IC reduced 50 % of IC reduced 100 % of IC reduced 25 % of SP reduced 50 % of SP reduced 100 % of SP reduced All trains reduced

ATO can even save more, namely 119 kWh (35 %). It should be mentioned, that these values do not correspond to realistic cases, since the comparison is done in a simulation tool where weather conditions, train driver behavior, gradients and curves are neglected. Nevertheless, an important finding of this consideration is that the ATO can save more energy relatively when operated on regional trains.

It can therefore be concluded, that ATO implemented on mainlines will save energy and increase the timetable compliance. The effects are different in scale depending on the train type: The effect of timetable compliance is bigger for long-distance trains while the amount of energy saving is bigger on regional trains.

2) Implementation of TMS In general, trains operated in OpenTrack without ATO will

follow the given timetable. However, timetable entries for stations, at which the train does not stop, are ignored. Therefore, the train path of an IC with or without TMS are exactly the same (see Fig. 7). Only the departure time at the initial station is respected. Since SPs have many stops where they respect departure times, a TMS has more control over such trains. They inherently follow the rescheduled timetable better.

Thus, if the trains are not controlled by ATO a TMS has very few control over long-distance trains while it can control regional trains to a certain extend.

3) Implementation of ATO and TMS In Fig. 8 the train path of a delayed IC is displayed for the

case with and without TMS and ATO, respectively. As mentioned earlier, the IC without ATO will drive the same

train path, regardless of application of TMS. If the IC is controlled by ATO but not by TMS, it will speed up until it has reached the original timetable. From that point on it will follow the timetable strictly and therefore arrive on-time rather than early. Now, if the train is controlled by both, ATO and TMS, the train will follow the rescheduled TMS timetable. This implies two effects: First, the train will arrive late, because the TMS estimates larger travel times than what is technically possible in the simulation tool. Second, the energy consumption in this case is lowest, since the train follows a slower travel time and is commanded to coast on certain parts.

From this can be concluded, that the TMS has its full effect on the traffic system only, if ATO commands the trains to follow the rescheduled timetable. However, as the TMS estimates the travel times from the original timetable, the trains will then arrive with a certain delay. Thus, it is to be expected that a traffic system with ATO and TMS will implicate both a decrease of conflicts and energy consumption but also an increase in delays, compared to systems without ATO or TMS.

B. Results of Simulations 1) Simulations regarding Q1 The expectation mentioned above is verified regarding the

punctuality at 3 min in the top of Fig. 9. The scenarios with TMS and ATO have the lowest punctuality, that is the most trains arriving with a delay larger or equal to 3 min. The reason for this finding is that the TMS assumes lower travel times than technically possible and the ATO commands the trains to follow these rescheduled timetables. Thus, trains operating with ATO and TMS will arrive with a certain delay, while trains without ATO and TMS will drive at full speed and arrive earlier than expected by the timetable. Again, it should be mentioned, that these latter trains neglect effects such as weather conditions, driver behavior, gradients and curves. Their minimal travel time is unrealistic and serves only as benchmark in this work. In any case it can be seen, that the punctuality is affected especially if IC are operated with ATO. This again confirms the large effect of the ATO on timetable compliance, when operated on long-distance trains.

Regarding the energy consumption in the middle of Fig. 9, equipping all trains with ATO leads to a reduction of 27 % when operating with TMS and 20 % when operating without TMS in comparison to the cases without ATO. Further, while there are fewer SPs (8) in operation than ICs (12), the amount of energy saved is similar. This corresponds to the two

Fig. 6. Energy consumption.

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findings, that on one hand the TMS has more control over SPs even without ATO, and on the other hand that ATO can save more energy on SPs than on ICs.

The amount of unexpected brakings throughout the train journey is displayed in the bottom of Fig. 9. Regarding the scenarios without ATO, operating with TMS leads to more conflicts than if operating without. The reason for this is, that SPs obey the rescheduled TMS timetable to a certain extend while the ICs almost completely ignore it. Therefore, two train types following different driving patterns operate on the same track. This leads to increased conflicts, e.g. if an IC gets stuck behind a SP on a single-track line. The same effect arises, if only SPs are equipped with ATO and ICs drive at full power. This again leads to an increased amount of conflicts due to different driving patterns.

2) Simulations regarding Q2 Two findings become immediately apparent when looking

at the punctuality in Fig. 10: First, the scenarios without any knowledge and the ones with ATO knowledge lead to the exact same results. That is because the first priority of the ATO is to arrive on-time at the next station. Since the train with reduced performance is always lagging behind the scheduled timetable, the ATO will always command this train to drive at maximum traction force. However, the train is not able to catch up due to the reduced performance, thus this situation stays the same during the entire train journey. It does not matter, whether the ATO can take into consideration the performance reduction or not. The second finding is, that in the scenarios with TMS knowledge the punctuality suffers considerably. This is again due to the fact, that the TMS estimates travel times, which are based on the original timetable. If the TMS takes into consideration performance reductions, it will schedule an even larger travel time for such

trains. This will lead to additional delays compared to the cases where the trains drive as fast as they can.

Considering the amount of unexpected brakings in the bottom of Fig. 10, it further becomes clear, that there is an issue with the cases in which ICs have reduced performance. This can be explained by the discrepancy between microscopic and macroscopic rescheduling. On the one hand, the TMS calculates the rescheduled timetables on a microscopic scale, i.e. it defines TPE for each block section. On the other hand, the ATO considers the macroscopic TPE points, i.e. the TPE between all stations. Therefore, it may happen, that the TMS schedules the SP with full performance to enter a single-track line before an IC with reduced performance. However, since the ATO neglects microscopic TPE points, the IC will be guided into the single-track line before the SP and the SP will face many unexpected brakings, as it is stuck behind the IC, which has reduced performance.

VI. CONCLUSION AND OUTLOOK The conclusion can be given as follows.

Q1 How does the penetration and distribution of ATO trains influence the traffic flow in mixed traffic systems?

Answer: Applying ATO to mainlines reduces the energy consumption and increases the timetable compliance. Because regional trains have more acceleration cycles, the ATO can save more energy relatively, since it can prevent acceleration to full speed between stations by coasting at the right time. On the other hand, due to the long uninterrupted travel times of long-distance trains, the compliance to the timetable can be improved to a great extend with ATO. The same is valid for freight trains, as they also have long travel times without intermediate stops. If an operator of a mixed traffic system intends to equip a certain part of trains with ATO, it therefore depends on the pursued objective. If the energy consumption is to be reduced, equipping regional trains with ATO might be more effective. If the objective is, however, to reduce conflicts and have more control over the train's trajectory, long-distance trains should be equipped with ATO.

Q2 How does sharing knowledge about train performance reductions influence the traffic flow?

Fig. 9. Results of simulations regarding Q1.

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Answer: If certain trains are reduced unexpectedly in performance for some reason, it makes no difference whether the ATO is informed about this or not. In both cases, the trains will be commanded to drive at maximum speed that is still possible in order to catch up to the given timetable. On the other hand, an informed TMS could reduce delays and conflicts. This however remains unclear in this work. In any case, the TPE points need to be implemented on a microscopic scale at critical locations in order to respect the given train orders. Further, the estimated travel time by the TMS should correspond to the realistic minimal travel time in order to prevent delays rather than provoking them.

Further research might develop and improve the simulations and evaluations done in this work in several aspects. To start with, the simulation framework can be extended with the implementation of an online multiple open loop or closed loop TMS, sending updated rescheduled timetables based on the current traffic state. Further, the TMS and ATO could be extended such that they themselves can detect and communicate delays and performance reductions, instead of giving those disruptions as input information before the simulations. In order to improve the results with regard to the TMS, the travel times need to be estimated not based on the original timetable but rather on the technically minimal possible travel time. Further, additional TPE points should be added at critical locations to respect the intended train order, e.g. before the corridor becomes a single-track line. It is further to be revised if the original dwell times at stations need to be fully respected in any case as it is planned here by the TMS, or if the trains can depart after a minimum dwell time has passed. Finally, the simulated scenarios can be extended for example by adding freight trains, increasing the time horizon, increasing the number of delay cases or adding a bad driver behavior as benchmark scenario.

ACKNOWLEDGMENT My first thanks go to both of my supervisors Dr. Pengling

Wang and Dr. Valerio De Martinis. I appreciated our meetings very much, in which they would always listen to my concerns, encourage me to try further possibilities and assist me with their great experience. Further, I wish to thank Prof. Francesco Corman and Prof. John Lygeros, who enabled me to do this work outside of my home department. Many thanks go to Dr. Xiaojie Luan for the efficient cooperation regarding the implementation of the traffic management system in my simulations. I would also like to thank Dr. Nadia Hürlimann

from OpenTrack for the uncomplicated cooperation enabling me to work from home as consequence of the corona pandemic. Finally, I thank my friend Patrick Althaus, who provided me with advice, suggestions and corrections.

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