Optimal Driving Profiles in Railway Systems based on Data

Envelopment Analysis

Achilleas Achilleos

, Markos Anastasopoulos

, Anna Tzanakaki

3,2

, Marius Iordache

Olivier Langlois

, Jean-Francois Pheulpin

and Dimitra Simeonidou

Institute of Accelerating Systems and Applications, Athens, Greece

Department of Electrical and Electronic Engineering, University of Bristol, U.K.

Department of Physics, National and Kapodistrian University of Athens, Greece

Alstom SA, France

Keywords: Smart Energy Metering, Data Envelopment Analysis, Optimal Driving Profiles, Railway System.

Abstract: The present study focuses on the development of a dynamically re-configurable Information Communication

Technology (ICT) infrastructure to support the sustainable development of railway network. Once data have

been collected, the extracted knowledge is used to develop a set of applications that can improve the energy

efficient operation of railway systems. A typical example includes the identification of the optimal driving

profiles in terms of energy consumption. In the present study, this is achieved through the adoption of an

optimization framework based on Data Envelopment Analysis (DEA). The performance of the proposed

scheme is evaluated based on actual data collected at an operation tramway system. Preliminary results

illustrate that when the proposed method is applied, a 10% reduction in the overall power consumption can

be achieved.

1 INTRODUCTION

The present study focuses on the identification of the

optimal driving profiles on tramway systems. To

achieve this, an experimental campaign has been

carried out to measure various parameters from an

operational tramway system. Data for this study was

collected via a dynamically re-configurable

Information Communication Technology (ICT)

infrastructure

to facilitate both the operation and the

Data Centers

Internet

EPC

PDN-

Data Centers

vBBU

RRH

Mobile Network Operator #1 Mobile Network Operator #2

LTE

LTE WiFi

Multi-technology Access Network

P-S GW/

GSN

Metro/Core

Optical

RRH

Heterogeneous Transport

Network

EventsWeather

Facilities

Sensors

Devices

MQTT, AMQP,

STOMP

Rest

APIs

CoAP

Storage

Processing

Analytics

Data Management Platform

LTE Macro

cell

SDN Controller

Management & Orchestration Services

Control

Figure 1: Converged Heterogeneous Network and Compute Infrastructures supporting railway services: Use case where data

are collected from various devices (1) are transmitted over a 5G network (2) to the cloud-based data management platform (3).

254

Achilleos, A., Anastasopoulos, M., Tzanakaki, A., Iordache, M., Langlois, O., Pheulpin, J. and Simeonidou, D.

Optimal Driving Proﬁles in Railway Systems based on Data Envelopment Analysis.

DOI: 10.5220/0007878000002179

In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2019), pages 254-259

ISBN: 978-989-758-374-2; ISSN: 2184-495X

end-user services supported by the railway network.

Despite the recent progress in railway smart metering

solutions and the implementation of several

experimental trials, optimal operation of the ICT

network supporting the power grid remains an

unsolved challenge, even in the case where

monitoring is limited to the rolling stock. The same

holds for the interoperability between different

heterogeneous network segments, which are currently

static and unaware of each other. As a result, Railway

System Operators (RSOs) are faced with a number of

options for building their ICT networks but are

limited by their inability to dynamically reconfigure

the network infrastructure according to their

operational and business needs and the lack of

benchmarking information between possible

solutions.

In response to these challenges we propose a

smart metering system that monitors the energy flows

of the whole railway systems and identifies the

optimal performance/cost trade-offs on the fly. This

is achieved through the deployment of an advanced

open Operational Data Management (ODM) platform

that comprises the following core elements

Anastasopoulos (2018):

a) A heterogeneous secure and resilient

telecommunication platform, consisting of both

wireless (e.g. Long Term Evolution – LTE, WiFi,

Satellite) and wireline (e.g. optical) systems

converging energy and telecom services. This

infrastructure is used to interconnect a plethora of

monitoring devices and end-users to the

Operational Control Center (OCC).

b) A platform that relies on a hybrid data storage and

processing mechanism combining state-of-the art

open source SQL/Non-SQL databases as well as

batch and stream processing engines. Based on the

characteristics of the collected data and the

selected applications, data are dynamically

forwarded to the most suitable storage/processing

platform. A high-level view of this process is

shown in Figure 1.

Once information has been collected, the

extracted knowledge is used to support a set of

applications that can improve the energy efficient

operation of railway systems. A typical example

includes the identification of the optimal driving

profiles in terms of energy consumption. In the

present study, this is achieved through the adoption of

an optimization framework based on Data

Envelopment Analysis (DEA). The performance of

the proposed scheme is evaluated based on actual data

collected from an operational tramway system. The

rest of the paper is organized as follows. Section 2

outlines the objectives of the proposed study, Section

3 gives a brief overview of the state of the art on the

subject, the research methodology along with a

description of the proposed scheme is provided in

Section 4. Finally, Section 5 concludes the paper.

2 OUTLINE OF OBJECTIVES

The main objective of this study is to improve the

energy efficient operation of tramway systems

through the identification of the optimal driving

profiles. To achieve this, a smart metering system has

been deployed monitoring energy, kinematic and

environmental parameters of an operational tramway

system based on sensing equipment installed both on-

board and at the trackside. Once data have been

collected and stored at the ODM system, an

optimization framework based on DEA has been

developed allowing the identification of the optimal

driving styles. The objective of this approach is to

identify driving styles that minimize the consumed

energy subject to set of constraints related to

scheduling, capacity and environmental conditions.

3 STATE OF THE ART

The problem of identifying optimal driving styles in

railway systems has been extensively studied over the

last years and a plethora of solutions have been

proposed. These include offline techniques based on

Integer Linear Programming (

Gallo, 2015), schemes

exploiting analytical kinematic equations and online

algorithms using Machine Learning techniques

(

Zhang, 2019). Another approach relies on the

Dynamic Programming Optimization Method

proposed in (Mensing, 2011). Other studies minimize

energy using Particle Swarm Optimization for a

catenary-free mass transit system (Chang, 1997).

In this work, a different approach compared to the

state-of-the art is adopted based on DEA. DEA can be

effectively applied to the railway sector to improve

service efficiency. Based on DEA, a linear

programming model can be developed that can

identify driving styles which can produce more output

(i.e. transfer a larger number of passengers in shorter

times) with less input requirements (i.e. power

consumption).

Optimal Driving Proﬁles in Railway Systems based on Data Envelopment Analysis

255

Table 1: Sample of the collected dataset.

Timestamp External

Temp

Speed Current

HVAC C2

Voltage

(catenary)

Current

(Ventilation)

Voltage

HVAC

Total

Energy

Panto

°C km/h A V A V kWh

1442729913 10.8 43 15.6 892 38.7 449.32 37.0573402

1442729914 10.8 40.8 15.6 891 37.9 449.28 37.00736674

1442729915 10.7 38.9 15.6 869 38.2 449.55 36.95579201

1442729916 10.7 36.9 15.6 874 38.2 449.64 36.90263086

1442729917 10.8 35.1 15.6 855 39.5 449.73 36.85689206

4 METHODOLOGY

4.1 Data Collection Process

To improve energy efficient operation of railway

systems, initially, an ODM platform has been

deployed enabling data collection and processing of

information obtained from a variety of sensors and

devices. This platform comprises a communication

segment that relies on a set of optical and wireless

network technologies to interconnect a variety of end-

devices and compute resources. Through this

approach, data obtained from various sources

(monitoring devices, users and social media) can be

dynamically and in real-time directed to the OCC for

processing. The wireless technologies comprise

cellular WiFi, LiFi and LTE networks to provide the

on-board and on-board to trackside connectivity. For

the trackside the to the OCC segment, information is

transferred over an optical network. The overall

solution is shown in Figure 1. As mentioned above,

this platform is used to monitor a variety of

parameters. An indicative sample of the collected

measurements is provided in Table 1. This dataset

includes information related to the geographic

location of the rolling stock, on-board CO2 levels that

is used to estimate the number of passengers, internal

and external temperature that is important for the

evaluation of the Heating Ventilation and Air-

conditioning system’s (HVAC) performance,

kinematic parameters (including acceleration and

speed) etc.

The smart metering solution also comprises an

Information Technology (IT) segment that is

responsible for the storage and processing of the

measurements. Storage is accommodated by hybrid

mechanism combining state-of-the-art open source

SQL/NoSQL databases while processing is executing

based on Apache Spark. Using purposely developed

algorithms, knowledge can be extracted from the

dataset which can assist railway system operators to

identify optimal train driving and scheduling profiles.

4.2 Model Description

In the present study, identification of the optimal

driving profiles is performed using DEA. DEA is a

very powerful service management and

benchmarking technique originally developed by

Chames, Cooper and Rhodes (1978) to evaluate non-

profit and public sector organizations. This is

achieved by measuring the productive efficiency of

the construction elements of these organizations,

namely, decision-making units (DMUs). DEA can

measure how efficiently a DMU uses the resources

available to generate a set of outputs. The

performance of DMUs is assessed using the concept

of efficiency or productivity defined as a ratio of total

outputs to total inputs. Note that efficiencies

estimated using DEA are relative, that is, relative to

the best performing DMU or DMUs (if multiple

DMUs are the most efficient). The most efficient

DMU is assigned an efficiency score of 1, and the

performance of other DMUs vary between 0 and 1

relative to the best performance.

To apply DEA in railway environments, driving

styles are treated as DMUs. Now, let 𝑆 be the set of

driving styles extracted from the dataset with 𝐗



,𝑖∈

𝑆, being the vector of inputs of style 𝑖 , with 𝑁

elements 𝑥



,𝑗∈𝑁. Let 𝐘



,𝑖∈𝑆 be corresponding

vector of outputs with size 𝑀 (𝐘



=[𝑦



,𝑦



,…,𝑦



Let also 𝐗



=[𝑥



,…𝑥



] be the inputs of the

driving style that we want to evaluate and 𝐘



[𝑦



,..,𝑦



] the output vector. Introducing

parameter λ



indicating the weight given to driving

style 𝑖 in its attempt to dominate Style 0, the measure

of efficiency 𝜃 of Style 0 is determined through the

solution of the following optimization problem:

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

256

Table 2: Sample of 10 routes used for the identification of the optimal driving profiles.

StyleID Inter-station Travelling

Time (sec)

Total Energy

(KW)

HVAC

(KW)

CO2

(Average ppm)

Temperature

1 73 3139.2726 44.872627 47.979189 11.301351

2 77 2665.796 47.293833 38.555385 13.503846

3 73 4601.6475 29.122982 42.172973 14.404054

4 74 3397.467 45.642488 41.707368 14.797368

5 73 3146.8157 44.755127 45.322297 14.97973

6 77 3549.4091 307.04326 42.435443 15.134177

7 78 3334.6836 48.084032 42.387342 14.173418

8 75 3090.6305 45.894299 54.406974 13.892105

9 68 4883.8277 41.379654 46.720725 13.031884

𝑀𝑖𝑛 𝜃

Subject to

𝜆



𝑥



∈

≤𝜃𝑥



,∀𝑗 ∈ 𝑁 (1)

𝜆



𝑦



∈

≥𝑦



,∀𝑗 ∈ 𝑀 (2)

𝜆



≥0∀𝑖∈𝑆

Constraint (1) limits the inputs of all other driving

styles below the inputs used by the reference model

0, while equation (2) selects the driving styles that

outperform style 0. The above problem is solved for

all driving styles to identify the most efficient one.

In the present study, the optimal driving styles

have been calculated taking as inputs parameters

related to the in-cabin CO2 levels, the external

temperature, the total driving time between adjacent

stations, the total power consumption as measured by

the pantograph and the power consumed by the

HVAC system. An indicative sample of the

parameters characterizing the driving styles is

provided in Table 2, while the corresponding linear

programming (LP) formulation considering only the

first two styles is given below:

LP for evaluating Style 1:

min 𝜃

subject to

47.979189𝜆



+ 38.555385𝜆



+ 42.172973𝜆



≥ 47.979189𝜃 (3.1)

11.301351 𝜆



+ 13.503846𝜆



+ 14.404054𝜆



≤ 11.301351𝜃 (3.2)

3139.2726𝜆



+ 2665.796𝜆



+ 4601.6475𝐿3

≤ 3139.2726 (3.3)

44.872627𝜆



+ 47.293833𝜆



+ 291.22982𝜆



≤ 44.872627 (3.4)

73λ1 + 77λ



+ 73λ



≤ 73 (3.5)



,λ



,λ



≥0

LP for evaluating Style 2:

min 𝜃

subject to

47.979189𝜆



+ 38.555385𝜆



+ 42.172973𝜆



≥ 38.555385𝜃 (4.1)

11.301351 𝜆



+ 13.503846𝜆



+ 14.404054𝜆



≤ 13.503846𝜃 (4.2)

3139.2726𝜆



+ 2665.796𝜆



+ 4601.6475𝐿3

≤ 2665.796 (4.3)

44.872627𝜆



+ 47.293833𝜆



+ 291.22982𝜆



≤ 47.293833(4.4)

73λ1 + 77λ



+73λ



≤ 77 (4.5)



,λ



,λ



≥0

4.3 Results

Solving the LP model for the styles shown in Table

2, the efficiency scores can be readily determined.

The relevant results are provided in Table 3.

A preliminary set of results indicating the driving

styles obtained when the DEA approach is adopted is

shown in Figure 2. When the system is optimized for

energy efficiency (green curve) the obtained driving

style is smooth. On the other hand, when the system

is optimized for shorter travelling times a higher

average speed and steeper acceleration levels are

Optimal Driving Proﬁles in Railway Systems based on Data Envelopment Analysis

257

Figure 2: Tramway speed as a function KM distance for various driving profiles.

Figure 3: Optimal driving profile obtained when the DEA method is applied (red line) and comparison with styles obtained

from measurements (grey lines).

Table 3: Efficiency scores for the driving styles shown in

Table 2.

Style ID Efficiency

score

1 0.8099

2 0.7312

3 0.6887

4 0.6902

5 0.7649

6 0.6746

7 0.67

8 0.8975

9 0.819

observed. A similar set of results are shown in Figure

3 where the optimal profile that minimizes the power

consumption under end-to-end scheduling and

passengers’ constraints is illustrated. When the

proposed method is applied, a 10% reduction in the

overall power consumption can be achieved.

In the method followed, the fastest routes were

compared, those with the highest consumption and

those with the slowest routes, respectively. In

addition, the similar time routes were compared to

each other to arrive at the above results. For example,

we notice that routes 1 and 5 reach their destination at

the same time and have almost the same consumption,

total and ventilation. However, CO2 levels in the

cabin are higher in the case of the first route, so more

passengers are transferred. Therefore, it is reasonable

to get the result that route 1 is more efficient than

route 5. Additionally, we notice that route 8 is more

efficient than route 1. Also, in these routes the

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

258

consumptions are similar, but we observe a

considerable increase CO2. As a result, the tramway

on route 8, with more passengers and lower

consumption, arrived later to the station compared to

route 1.

5 CONCLUSIONS

The present study proposed a modelling framework

based on Data Envelopment Analysis that aims at

identifying the optimal driving styles in terms of

energy efficiency of an operational tramway system.

To achieve this, in the first stage of the research, a

data management platform has been deployed

enabling collection and monitoring of energy,

kinematic and environmental parameters.

Preliminary results indicate that the proposed

approach can reduce the energy consumption in

railway systems by 10%. A main limitation of this

approach is related to its increased computational

complexity. To address this, in our future work the

DEA method will be coupled with machine learning

techniques to reduce the complexity of the ILP

formulations.

ACKNOWLEDGEMENT

In2Stempo project has received

funding from the Shift2Rail Joint

Undertaking under the European

Union's Horizon 2020 grant agreement no 777515.

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