An Algorithm for Event Detection based on a combination of Loop and Journey Time Data

1. An Algorithm for Event Detection based on a combination of Loop and Journey Time Data Pengjun Zheng, Mike McDonald and David Jeffery Transportation Research Group University of Southampton

2. Contents Event vs. Incident Event detection for information purposes Advantages of Loop Data The proposed algorithm Preliminary results and conclusions

3. The National Traffic Control Centre (NTCC) (1) • is a large-scale project with a budget of EUR212 million. • uses advanced technology from Serco • operates 24 hours/day, 7 days/week

4. The National Traffic Control Centre (NTCC) (2) • Covers the Strategic Road Network (motorways + trunk roads) in England. • Some 1,000 CCTV cameras and 4,000 road sensors stream images and data into the facility • Also receives information from HA traffic officers, police, local authorities and weather centres.

5. Event vs. Incident (1) • Unplanned Event detection is one of the key services provided by the NTCC • Unplanned Events can be defined as all events, except Planned Events (e.g. roadworks), having duration of greater than a fixed threshold that could potentially have a material affect on the operation of the Project Network. • Events are identified in the NTCC by a significant increase of Travel Time lasting for a certain amount of time. • Events are therefore always associated with excessive delays to travellers in the NTCC.

6. An Event

7. Event vs. Incident (2) • How an incident is defined is inconsistent throughout the literature. • An incident is an unexpected event that temporarily disrupts the flow of traffic on a segment of roadway (Solomon 1991). • An incident is an event leading or likely to lead to changes in traffic patterns or behaviour resulting in changes in the driving context over some reference period (system). • It can be argued that any occurrence is an incident provided he/she wants to be aware of it (ie. If no action is to be taken by the control authority the event is not classed as an incident) (operator). • Anything that might get in his way and cause him inconvenience or delay (driver)

8. Event vs. Incident (3) • Incidents do not necessarily cause congestion. • e.g. Stationary vehicles in the ‘hard shoulder’ lane. • Events are incidents that cause delays

9. Event detection for information purposes (1) • The main purpose of event detection in the NTCC is to disseminate traffic information to the public about actual or likely delays. • Many incident detection methods cannot be directly applied in the NTCC, e.g. a stationary vehicle in the ‘hard shoulder’ lane is unlikely to cause an event. • Whilst for the incident detection, minimising the response time is crucial in several aspects. • Faster treatment for the injured. • Minimising the traffic flow disruption (and potential for secondary incidents).

10. Event detection for information purposes (2) • Events are recognised based on Travel Time Increases. • Significant increase in Measured Travel Time: The occasions that a Measured Travel Time (MTT) was greater than the Expected Travel Time by more than 12 minutes for each congestion event with MTT greater than the Standard Travel Time by more than 40%. • Lasting for a certain period: MTT greater than the Standard Travel Time for more than 15 minutes • Detection of Events is achieved by setting alerts at lower thresholds.

11. Event detection for information purposes – Problems • False Alarms. • A reduction in the threshold will result in the identification of a larger number of events that exceed the new threshold. • Timing. • Some Measured Travel Times may increase very quickly to exceed the threshold, which will reduce the effectiveness of using a lower threshold. • No Measured Travel Times are obtained if the road is totally blocked

12. Example (false alert) Event Not Events TT (s) TTprofile . JTs (s) Loop Speeds (km/h) Time (in 5 minute interval, 24:00 =288)

13. Example (timing) JT(s) JT(s) V(km/h) Time (in 5 minute interval, 24:00 =288)

14. Advantages of Loop Data • Independent source of information not used in the event definition • Potential very early alert. • Can pinpoint event location better • Provide additional information on the nature of the event e.g. whether it is a capacity reducing or demand increasing type.

15. An Event JT(s) JT(s) V(km/h) Time (in 5 minute interval, 24:00 =288)

16. Limitations of Loop Data • Not all events (ie causing a significant increase in TT) can be detected with loop data (e.g. loop not available, events without significant reductions in speeds) • Not all significant reductions in loop speeds are events (e.g. false alerts not achieving material affects level)

17. Loop speed data not available JT(s) JT(s) V(km/h) Time (in 5 minute interval, 24:00 =288)

18. An event without loop speed reductions

19. A ‘false’ alert

20. The algorithm (1) Any Travel Time increases should be a result of some speed reductions, such speed reductions can usually be detected by one or several loops within the Travel Time Section earlier than the increase of Travel Time.

21. The Algorithm (2) TT/TTstandard>=1.4 To Trig Alert TT-TTprofile>=420 (360) s Vfree/V>=1.3 for 10 Min TT/TTstandard<1.4 To End Alert TT-TTprofile<420 for 15 Min Vfree/V<1.3 for 15 Min

22. TT>=1.4TTstandard TT>=1.4TTstandard TT-TTprofile>=600s Event TT>=1.4TTstandard TT-TTprofile>=360s Vfree /V>=1.3

23. Results (1) • Total number of identified events: 68, of which 39 events are accompanied by speed reductions in the loop data. • Total number of Alerts: 68, of which 39 are Events, i.e. all events with accompanying speed reduction are detected. • 37 events detected within 10 minutes or before, 2 events detected 5 minutes before. • The probability that an alert is ‘good’ is 54%.

24. Results (2) • 14 events that were not identified by ‘Congestion Alert’ on time can be identified. • The false alerts are infrequent compared with JT only method.

25. Suggestions • Can detect nearly all events with accompanying speed reductions at low false alarm rate and well before other detection methods. Event detection based on a combination of loop and TT data is a good practice

26. Suggestions • The algorithm using loop information will not affect the other algorithm. • If combined with Journey time only algorithm (with a low threshold), the detection rate could be very high (close to 100%) Events without accompanying speed reductions can still be detected using Travel Time based methods

27. Future Work • The definition of ‘event’ could be optimally determined based on public opinion of traffic information requirements. • It may be beneficial to disseminate some ‘false’ events if such have been confirmed from other sources . • The algorithm can be further developed to enable variable thresholds.

28. Questions …