automatic transport network matching using deep learning
TRANSCRIPT
Automatic Transport Network Matching using Deep Learning
Manuel Martin Salvador, Marcin Budka, Tom Quay
European Transport Conference 201704/09/2017 - Barcelona
discoverpassenger.comwearebase.combournemouth.ac.uk
Problems to solve
- Passenger counting (real-time and historical)- Match passenger feedback to a particular vehicle
- Negative: bus is dirty, wifi is not working- Positive: bus driver has been very friendly
- Customer profiling based on ticket usage and frequent routes- Micro-targeting campaigns- Pro-active notification of disruptions
Hardware infrastructure is expensive
- Counting sensors in each door- On-board processing unit in each bus- Antennas to send data- Bluetooth beacons
Source: Infodev
Apps as infrastructure
- Mobile tickets are replacing paper tickets and smartcards- Smartphones are powerful devices with many sensors- Almost everybody owns a smartphone
Source: Synopsys
Crowdsensing
Aggregating data of many individuals
What’s network matching?
Match timestamped GPS trace to a bus line
9:30 9:31 9:329:33
9:34
9:35
Bus line??
Challenges
9:30
9:31
9:32
9:35
Missing data
Noisy locations
Spatio-temporal overlappings
Bus delays
?
?
Our approaches to network matching
Data collection
- We asked app users to share their journeys and indicate in which line they were travelling.
- We collected about 200 journeys and kept 164 journeys after manual cleaning.
- Length of the journey varied from 6 to 56 minutes.- 1 GPS point every minute.
Heuristic
1. Find nearest stops to points.2. Get list of candidate lines based on
location.3. Verify candidates based on
direction and time.4. Return most likely line+direction.
Problems of heuristic
- Slow → not scalable- Based only in timetabling information and bus stop positions
- In real life, GPS points might not be close to bus stops, and buses are delayed- We need a model able to cope with uncertainty
Deep Learning approach
Build a classifier based on input data.
Sequence classification:
(lat1, lon1, time1), …, (latn, lonn, timen) → label
Classic machine learning approaches don’t work with sequential data of different lengths. Let’s try with Recurrent Neural Networks!
Recurrent Neural Network (RNN)
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Recurrent Neural Network (RNN)
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(latt, lont, timet) (lat0, lon0, time0) (lat1, lon1, time1) (latn, lonn, timen)
line & direction line & direction line & directionline & direction
Challenges
- Needs loads of compute- Not enough real data with feedback- Noise due to:
- Low GPS accuracy- Bus delays- Missing points
3XS Deep Learning G10
● +15 million journeys● Covering the whole operator network -- at all times!● Start from 1 bus stop and track every minute● Random GPS accuracy based on a real distribution● Simulation of bus delays
Generating (lots of) data
Generating (lots of) data
Experimental setup
● Goal: maximise classification accuracy.● Transport network made of about 140 buses serving 23 lines.● Number of classes: 46 (23 lines x 2 directions).● RNN is trained over 15 million sequences of synthetic journeys.● Sequence length between 5 and 60 minutes.● Google’s TensorFlow 1.3 on NVIDIA GeForce 1080 and Titan X GPUs.● RNN cell type: GRU and LSTM.● Number of layers: between 1 and 5.● Cell size: 256, 512 and 768.● Real test set: 164 journeys.
Prequential test accuracy on synthetic data
In top 2 predictions? In top 3 predictions?Right prediction?
Overlappings! 37% of stop to stop segments have 2 or more lines.
Classification results on the real test set
Conclusion and future work
Promising results:
● Best approach: 68% accuracy (RNN GRU 2 layers; cell size: 768; with embeddings).● Up to 93% accuracy on best of 3 predictions.
Future work:
● Training data from real vehicle journeys instead of only timetables.● Experiment with different sampling rates (currently 1 per minute).
