Disruptions on Road Networks: Impact on traffic characteristics

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


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


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.


Study Area

What is the impact of disruptions on road network performance metrics?

Field Data collected:• Speed• Volume• Travel Time• Incident


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


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)


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.


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


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.


Results: Travel Time Assessment

Similarity between incident and no incident conditions



Volatility of travel times increases under incident conditions



Statistically Non-Competitive Parallel Routes

Statistically Competitive Parallel Routes


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


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.


Results: Volume Assessment

There are shifts in traffic volume proportions between routes


Results: Volume Assessment

Shifts of 2-3% in magnitude


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


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


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.


Future Research Impact

Develop new network

modelling methodology and

tools

Provide additional tools to assist in

decision making

Obtain a more sustainable

transport future


Questions?

Thank You

Disruptions on Road Networks: Impact on traffic characteristics

Education

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