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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