BIG DATA ANALYSIS IN RAILWAY DELAYS · data analysis Micro-simulation About the PhD Research...

BIG DATA ANALYSIS IN RAILWAY DELAYS Fabrizio Cerreto

PhD student

Transport modelling

DTU Management Engineering

DTU Management Engineering, Technical University of Denmark

Analytical model (delay propagation)

Empirical data analysis

Micro-simulation

About the PhD Research Project

2 BIG DATA ANALYSIS IN RAILWAY DELAYS 15 May 2017

UNDERSTANDING DELAYS IN RAILWAYS

•Realized running times real timetable supplements

•Earliness of trains

•Delay profiles clustering

DTU Management Engineering, Technical University of Denmark

Background – research motivation

Timetable allowance

•Running time supplements

•Headway buffers

Practical design:

• Good practices for magnitude (e.g. Capacity consumption – UIC406)

• Rule of the thumb for distribution (national rules: uniform, concentrated)

3

Understanding delays

•Causes

•Recurrent patterns

Robust design

• Timetable supplements

• Headway buffers

• Primary delay prevention

BIG DATA ANALYSIS IN RAILWAY DELAYS 15 May 2017

Foto: René Strandbygaard

DTU Management Engineering, Technical University of Denmark 15 May 2017

Planned Vs. Real timetable allowance

PLANNING:

Scheduled running times

OPERATION:

Realized running times and possible recovery

5 BIG DATA ANALYSIS IN RAILWAY DELAYS

Estimated minimum running

times

• Analytical formulation

•Microsimulation

•On-site tests

Planned Running time supplements

•Margin

•Rounding up

• Scheduled waiting times

Real minimum running time

•Rolling stock

• Infrastructure

•Other factors

Real timetable margin

•Actual possible recovery

•All the components together

ESTIMATION ACCURACY

UNCERTAINTY

DTU Management Engineering, Technical University of Denmark 6

•Q3 2014

Time frame

•København H – Roskilde

•~30 km

Line

•Semi-periodic timetable

•Express trains from/to Copenhagen

Traffic

•Heterogeneous

•Most important section

•Freight +Regional + National + International

•High interest from authorities

Reasons

BIG DATA ANALYSIS IN RAILWAY DELAYS 15 May 2017

Vestbane: Copenhagen - Roskilde

DTU Management Engineering, Technical University of Denmark 15 May 2017

11,8 km 15,6 km 3,9 km

Previous results: Copenhagen – Roskilde Realized Running Times Actual Running time supplements

7

0

100

200

300

400

500

600

700

800

900

1000

0 10 20 30 40 50 60 70 80 90 100

Runnin

g tim

es [

s]

Percentiles

Trains FROM Copenhagen

KH VAL VAL HTÅ HTÅ RO

0

100

200

300

400

500

600

700

800

900

1000

0 10 20 30 40 50 60 70 80 90 100

Runnin

g tim

es [

s]

Percentiles

Trains TOWARDS Copenhagen

RO HTÅ HTÅ VAL VAL KH

2nd percentile 2nd percentile

BIG DATA ANALYSIS IN RAILWAY DELAYS

DTU Management Engineering, Technical University of Denmark 15 May 2017

Previous results: Roskilde – Copenhagen Frequent delay patterns

8 BIG DATA ANALYSIS IN RAILWAY DELAYS

Valb

y

Høje

Tåstr

up

Copenhagen

Roskilde

11,8 km 15,6 km 3,9 km

DTU Management Engineering, Technical University of Denmark 10 BIG DATA ANALYSIS IN RAILWAY DELAYS 15 May 2017

• K14: 15/12/2013 – 14/12/2014

Time frame

• København H – Helsingør

• ~50 km

Line

• Cyclic timetable

• Well isolated

• High interest from authorities

Reasons

Kystbane: Copenhagen - Helsingør

DTU Management Engineering, Technical University of Denmark

Delay and Delay change profiles

BIG DATA ANALYSIS IN RAILWAY DELAYS 12 15 May 2017

Train 1309 - 22/4/2014

Absolute delay

Delay change

DTU Management Engineering, Technical University of Denmark

Pooled data, example Northbound ØK

BIG DATA ANALYSIS IN RAILWAY DELAYS 13 15 May 2017

Absolute delay

Delay change

DTU Management Engineering, Technical University of Denmark

Clustering: K-means

Mining historical delay data in railways

15 19 May 2017

•Simple

• Fast

•Converges almost always

• k must be chosen - metrics

•Clusters not fixed, no reference

DTU Management Engineering, Technical University of Denmark

Clustering on Delay change NORTHBOUND trains

BIG DATA ANALYSIS IN RAILWAY DELAYS 16 15 May 2017

Delay change

Absolute delay

DTU Management Engineering, Technical University of Denmark

Clustering on Delay SOUTHBOUND trains

Mining historical delay data in railways

18 19 May 2017

Absolute delay

Delay change

DTU Management Engineering, Technical University of Denmark BIG DATA ANALYSIS IN RAILWAY DELAYS 19 15 May 2017

Conclusions

Real running time supplement Vs. Scheduled

• It is possible to identify the actual running time supplement in railway schedule based on realized running times

Earliness of train

• Trains that travel early are an issue

• Too large supplement times are counterproductive

Recurrent delay patterns

• Towards bottlenecks - Delays changes are distributed

• From bottlenecks - Delays changes are concentrated at the bottleneck

Next steps

• Regression/Classification into clusters: Period of the day, Period of the year, Weekday, Composition, Composition changes

• Big data for dynamics in delay propagation

Thanks for your attention Fabrizio Cerreto

PhD student

Transport modelling

DTU Management Engineering

Phase-based Planning for Railway Infrastructure Projects

Rui Li

DTU Management, Technical University of Denmark

Den Danske Banekonference

May 15th, 2017

2

Agenda

• Challenges

• Phased-based planning

• A tamping case study

• Conclusions

3

Challenges

• Increasing demands

–More trains per hour

–Longer operation hours

–Higher speed etc.

Increased asset use -> more maintenance

•Constraints

–Limited railway infrastructure

–Decreased track possessions

–Limited resources (time, working-force, machinery etc.)

–Tight budget

It requests an effective planning

4

Challenges - Planning is a complex task

• There are many impact factors need to be considered

A systematic planning approach is needed

5

Phase-based Decision Support System

6

A case study: preventive tamping

• Odense–Fredericia (Od-Fa)

– Double track

– 57.2 km

– Main line (max: 180 km/h)

– Passengers & Freights

– 220 sections

– 3 years planning horizon

7

Track degradation and tamping

8

• A predictive model has been integrated to forecast track degradation

Initial track quality, degradation, and threshold

9

TeO: Technical Optimization – to identify the tamping needs

But it is neither efficient nor cheap.

The schedule from TeO

10

EcO: Economic Optimization Model – Covering the tamping needs with

less costs

The schedule from EcO

11

The schedule from CoO

CoO: Constrained Optimization Model

12

Phase-based Decision Support System

13

Conclusion

• Identifying the need of tamping maybe is easy, but scheduling is art

• A perspective of predictive planning (long term planning) can improve

maintenance economy

• Phase-based decision support system can help to improve planning

14

The overview of the research

15

Thank you!

ruli@dtu.dk