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Disruptions on Road Networks: Impact on traffic characteristics

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Kasun Wijayaratna & Kenneth Lam

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Disruptions on Road Networks: Impact on traffic characteristics

  1. 1. Disruptions on Road Networks: Impact on Traffic Characteristics Kasun Wijayaratna and Kenneth Lam
  2. 2. AITPM National Conference, Sydney, 2016 2 Outline • Motivation • Key Objectives • Methodology • Results • Findings Source: http://news.carrentals.co.uk/wp-content/uploads/2011/12/Sydney- Traffic.jpg
  3. 3. AITPM National Conference, Sydney, 2016 3 Motivation: Investigating “short-term” disruptions • Congestion – The reliance and dependence on road transport across the years has resulted in widespread congestion of road networks o Recurrent Congestion: “Peak Periods” (expected and generally predictable) o Non-Recurrent Congestion: “Disruptions” (uncertain and unpredictable) • The impact of non-recurrent congestion – Users must allocate 3 times the travel time of free flow conditions to ensure that they can achieve on-time arrival and account for the possibility of uncertain events occurring (Schrank et al., 2012). • Why only short-term disruptions? – Long-term disruptions are related to catastrophic events which have a significant impact but low probability of occurrence o Short-term disruptions affect day-to-day operations of a road network (higher probability of occurrence with potential to cause considerable delays). o Lack of empirical studies concerning short-term disruptions
  4. 4. AITPM National Conference, Sydney, 2016 4 Key Objectives • Investigate the impact of short-term incidents on travel time during peak period traffic conditions on parallel commuter routes. • Construct ‘no incident’ and ‘incident’ data subsets for peak traffic conditions to: – Compare travel time variability – Compare the route choice behaviour of users (traffic volume data) – Investigate whether there is adaptive behaviour of road users. • Highlight the implications of the findings in the context of current transport planning and traffic management approaches and to identify possible future directions to better account for incidents on a road network.
  5. 5. AITPM National Conference, Sydney, 2016 5 Study Area What is the impact of disruptions on road network performance metrics? Field Data collected: • Speed • Volume • Travel Time • Incident
  6. 6. AITPM National Conference, Sydney, 2016 6 Methodology Data Types • Average Speed Data • Traffic Volume Data • Incident Data • Crash Data • Length of Link Data Data Synthesis • Generation of ‘Incident’, ‘Non-Incident’ Subsets for Speed, Travel Time and Volume • Generation of travel time dataset Average Speed Analysis • Plots of Incident and Non-incident Speeds Travel Time Analysis • Plots of Incident and Non-Incident Travel Times • Statistical Testing of Incident and Non-Incident Travel Times (T-Test) Traffic Volume Analysis • Analysis of Traffic Counters Values along routes Preliminary Assessment (Literature Review) Route Pair Selection Obtain RMS Data for selected Routes Pairs Sort and Preprocess data to create data subsets Average Speed Analysis Travel Time Analysis Traffic Volume Analysis Discussion of Results Conclusion
  7. 7. AITPM National Conference, Sydney, 2016 7 Data • RMS provided the following data sets, across all route pairs, for the period between January 2012 and June 2013. – Average speed data (GPS fleet vehicle data aggregated at 15 minute intervals) – Categorised incident data – Hourly traffic volume data – Link length data (used to estimate travel time in conjunction with speed data)
  8. 8. AITPM National Conference, Sydney, 2016 8 Data Pre-Processing • Determination of relevant days and time periods of data – Elimination of weekends and public holidays – Peak period assessments (6am – 9am and 4pm – 7pm) • Removal of outlier data – Inaccurate or incomplete measurements (exaggerated speed/travel time measurements due to short link lengths) – Lack of incident data for 5 out of 10 route pairings • Separation of traffic data to develop ‘incident’ and ‘no-incident’ data sets.
  9. 9. AITPM National Conference, Sydney, 2016 9 Final Routes Set 1 Set 2 Set 3 Set 4 Set 5 1A: Cumberland Highway 1B: Hume Highway – Horsley Drive 2A: Windsor Road 2B: Old Windsor Road 3A: Parramatta Road – St Hillier’s Road 3B: Centenary Drive – Hume Highway 4A: Parramatta Road 4B: Western Distributor 5A: Princes Highway 5B: Rocky Point Road
  10. 10. AITPM National Conference, Sydney, 2016 10 Statistical Analysis • Descriptive Statistics – Mean – Median – Standard Deviation • Hypothesis Testing – Are the mean route travel times during non-incident conditions ( 𝑇1) different to the mean route travel times during incident conditions (𝑇2)? – Null Hypothesis: 𝑇1 − 𝑇2 = 0 – Alternative Hypothesis: 𝑇1 − 𝑇2 ≠ 0 – Test assessed using Welch’s T-test. • Route Utilisation – Compare proportion of vehicles on each route in no-incident and incident conditions.
  11. 11. AITPM National Conference, Sydney, 2016 11 Results: Travel Time Assessment Similarity between incident and no incident conditions
  12. 12. AITPM National Conference, Sydney, 2016 12 Results: Travel Time Assessment Volatility of travel times increases under incident conditions
  13. 13. AITPM National Conference, Sydney, 2016 13 Results: Travel Time Assessment Statistically Non-Competitive Parallel Routes Statistically Competitive Parallel Routes
  14. 14. AITPM National Conference, Sydney, 2016 14 Selected Travel Time Results: Set 4 Travel Time (mins) AM Peak (6am – 9am) PM Peak (4pm – 7pm) Route (Westbound) No Incident Incident on Western Distributor Incident on Parramatta Road No Incident Incident on Western Distributor Incident on Parramatta Road Western Distributor 13.13 13.60 13.23 12.98 13.10 13.07 Parramatta Road 13.44 13.52 13.89 13.76 13.85 13.83 Travel time is stable across incident and no incident scenarios - Consistent with equilibrium concepts used in transport planning models
  15. 15. AITPM National Conference, Sydney, 2016 15 Unexpected Result: Stability of Average Travel Times • Disruptions result in delays which should increase travel time. • Why is it not the case here? – Instances of minor disruptions which do not necessarily increase travel time. – Unrecorded incidents resulting in an inflation of travel time within the ‘no incident’ data sets and a deflation of travel time within the ‘incident’ data sets. – Cases where travel times are unusually high due to the volatility in demand.
  16. 16. AITPM National Conference, Sydney, 2016 16 Results: Volume Assessment There are shifts in traffic volume proportions between routes
  17. 17. AITPM National Conference, Sydney, 2016 17 Results: Volume Assessment Shifts of 2-3% in magnitude
  18. 18. AITPM National Conference, Sydney, 2016 18 Selected Traffic Volume Results: Set 4 Proportion of Route Usage (Volume %) AM Peak (6am – 9am) Route (Westbound) No Incident Incident on Western Distributor Incident on Parramatta Road Western Distributor Occupancy 51.21% 48.98% 54.55% Parramatta Road Occupancy 48.79% 51.02% 45.45% Adaptive routing carried out by users to avoid disruptions
  19. 19. AITPM National Conference, Sydney, 2016 19 Limitations and Improvements • Missing and erroneous data – Inaccuracies of speed data obtained from GPS technology  Potential to use Bluetooth or specific speed survey data • Lack of direct travel time data – Travel time data was calculated using speed data and link length data  Conduct travel time surveys or use Bluetooth data • Small sample size issues – Low numbers of incidents on some routes led to small sample sizes  Define different route pairings which contain greater numbers of incidents • Utilisation of representative volumes – Coarse volume data measured on an hourly basis  Obtain 15 minute volume data from specific flow/tube count surveys • Lack of Origin-Destination data – There are assumptions about vehicles travelling the extent of the route  Conduct origin-destination surveys
  20. 20. AITPM National Conference, Sydney, 2016 20 Key Findings • Findings from the empirical analysis: Equilibrium concepts can not be dismissed in transport modelling (similarity of travel times) Adaptive Behaviour is present, in light of an incident a traveller will choose an alternative available route (changes in % occupancy between competing routes)  Highlights the need for adaptive equilibrium frameworks to model road traffic in order to appropriately account for disrupted conditions.
  21. 21. AITPM National Conference, Sydney, 2016 21 Future Research Impact Develop new network modelling methodology and tools Provide additional tools to assist in decision making Obtain a more sustainable transport future
  22. 22. AITPM National Conference, Sydney, 2016 22 Questions? Thank You

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