disruptions on road networks: impact on traffic characteristics
TRANSCRIPT
Disruptions on Road Networks: Impact on Traffic Characteristics
Kasun Wijayaratna and Kenneth Lam
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
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 networkso 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 occurrenceo 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
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.
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
AITPM National Conference, Sydney, 2016 6
MethodologyData 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 SpeedsTravel 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
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)
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.
AITPM National Conference, Sydney, 2016 9
Final Routes
Set 1
Set 2
Set 3
Set 4
Set 5
1A: Cumberland Highway1B: Hume Highway – Horsley Drive
2A: Windsor Road2B: Old Windsor Road
3A: Parramatta Road – St Hillier’s Road3B: Centenary Drive – Hume Highway
4A: Parramatta Road 4B: Western Distributor
5A: Princes Highway
5B: Rocky Point Road
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 () different to the
mean route travel times during incident conditions ()?– Null Hypothesis: – Alternative Hypothesis: – Test assessed using Welch’s T-test.
• Route Utilisation– Compare proportion of vehicles on each route in no-incident and incident
conditions.
AITPM National Conference, Sydney, 2016 11
Results: Travel Time Assessment
Similarity between incident and no incident conditions
AITPM National Conference, Sydney, 2016 12
Results: Travel Time Assessment
Volatility of travel times increases under incident conditions
AITPM National Conference, Sydney, 2016 13
Results: Travel Time Assessment
Statistically Non-Competitive Parallel Routes
Statistically Competitive Parallel Routes
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
RoadNo
IncidentIncident 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
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.
AITPM National Conference, Sydney, 2016 16
Results: Volume Assessment
There are shifts in traffic volume proportions between routes
AITPM National Conference, Sydney, 2016 17
Results: Volume Assessment
Shifts of 2-3% in magnitude
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 RoadOccupancy 48.79% 51.02% 45.45%
Adaptive routing carried out by users to avoid disruptions
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
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.
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
AITPM National Conference, Sydney, 2016 22
Questions?
Thank You