www-civil.monash.edu.au/its Institute of Transport Studies National Urban Transport Modelling Workshop, 5 March 2008 Travel Demand Management Geoff Rose Director, ITS (Monash) Transport Theme Leader, Monash Sustainability Institute

2 Institute of Transport Studies Presentation Outline Introduction Has the scope of TDM options changed over time? What range of demand responses needs to be modelled? What are the variables influencing travel choices? Modelling issues to be addressed

3 Institute of Transport Studies Introduction Under congestion, marginal reductions in demand can have a large impact on average cost and also potentially on variability (system reliability) –Demand and supply side modelling tends to focus on the average rather than variability around the average TDM = Tinkering and Diddling at the Margin?

4 Institute of Transport Studies Range of TDM Measures Wayte’s 1991 categorisation of 4 strategy areas is still valid –Improved Asset Utilisation >Peak Spreading: staggered/flexible hours, cost/toll/availability differentials >Vehicle Occupancy: carpooling, HOV lanes, park and ride –Physical Restraint >Area: cells, mazes, cordon collars >Link: access metering, PT priority >Parking Limitations: space limits & access controls –Pricing >Road Pricing: tolls, congestion pricing, >Parking Prices & Taxes: fuel & parking taxes, car ownership taxes/charges –Urban and Social Changes >Urban Form: compact cities, efficient urban development >Social Attitude: voluntary travel behaviour change >Technological Change: communications substitutions

5 Institute of Transport Studies Has the scope of TDM options changed over time? Emerging from Voluntary Travel Behaviour Change era –Limited systematic attention to ‘stick’ measures, particularly parking related, as well as broader taxes/charges Recognise geographic range of applications spans from individual building/site, group of sites, link, route, corridor to area/region –Experience limited primarily to either end of the spectrum Scope for greater packaging of TDM measures –To address induced demand effects of infrastructure investment –‘Carrot’ + ‘Stick’ measures Technology making more options feasible: pricing (HOT lanes, distance based insurance and road pricing), access control, car-sharing Modal coverage of TDM is changing –No longer just road demand management also public transport –Carpooling to increase accessibility not just manage congestion –Walking and cycling now under the TDM tent –Growing interest in TDM for freight

6 Institute of Transport Studies What range of demand responses needs to be modelled? Mobility and Lifestyle –Employment, Housing, Activity program, car ownership, IT options accessibility Activity and Travel Scheduling –Acquire pre-trip information –Activity schedule/trip frequency/no travel (tele- services), tour type, departure time, destination, travel mode, route Activity and Travel Re-Scheduling –Acquire en-route information –Activity, destination, travel mode and route switching

7 Institute of Transport Studies Can models capture the range of variables influencing demand? Traditional inputs: –In-vehicle and out-of-vehicle time –Costs (out of pocket) –Vehicle availability –Demographic variables: gender, income, HH size, license Need capacity for: –Market segmentation >Health as a motivator for active transport choice >Environmental awareness (climate change) –Impact of vehicle ownership & operating costs (role of FBT) –Changing demographics (acceptance and use of technology)

8 Institute of Transport Studies Existing modelling capabilities Factoring down vehicle trip matrix Quantifies change in congestion but not the reason for the change Used in benefit estimation for TravelSmart What change in (perceived) Generalised Cost would result in an X% reduction in car use If the focus was market segments (work trips in an area, school trips), could help to set targets for TDM to design packages of measures to achieve desired change in congestion

9 Institute of Transport Studies Existing modelling capabilities Pivot point models Elasticity based CUTR Average Vehicle Ridership (AVR) Model predicts change in AVR for a selected incentives Demand for High Occupancy Toll Lanes Increasing experience in the private sector modelling (particularly in the USA)

10 Institute of Transport Studies Modelling issues requiring attention 24 hour assignments with time period factoring –Difficult to examine trip timing decisions >peak/off-peak switch: change in congestion versus emissions versus VKT ? –Which outputs are of interest? Trip chaining Carpooling and carsharing Network representation –Zone size: intrazonal trips – school, walk and bike trips –Links >Local streets not coded into strategic models >Bike paths not usually included in networks >(Bike) congestion on on-road facilities

11 Institute of Transport Studies Modelling issues requiring attention Model re-estimation –Changes in parameter values following changes in perceptions e.g. TravelSmart >Value of data from TDM evaluations in Austroads depository Recognising uncertainty in inputs and modelled effects –Scenarios and forecast ranges Role of hybrid models linking micro-simulation and strategic models to vary network magnification

12 Institute of Transport Studies Closing comments Limited experience to date with modelling the impacts of TDM initiatives Data from evaluations of TDM initiatives needs to be shared to facilitate ‘model’ development Scope to enhance practice in the short term while more fundamental model development required in the longer term