Incorporating Peak Spreading into a WebTAG Based Demand Model

Transcription

1 Incorporating Peak Spreading into a WebTAG Based Demand Model Presented by: Philip Clarke Modelling Director [email protected] Contents 1. Introduction and History of the Model 2. The Full Model Structure 3. Highway Assignment Model Requirements SATURN 4. The Parking Model 5. The Peak Spreading Model 6. Model Calibration Statistics 7. Model Tests 8. Conclusions 9. Questions 1

2 Introduction and History Case Study : The Truro Strategic Transport Model Originally developed a traditional four stage model for a major scheme business case (2005) SATURN Assignment Models (3 time periods) Visual-tm Demand Model Disaggregate Parking Model developed which was connected to the four stage model (2005) Converted model into WebTAG compliant demand model structure (2006) Academic development of the model: ETC 2006 Modelling Congestion with Travel Derived ed From Activities ities ETC 2007 Using Activity based modelling to Implement a Peak Spreading Model in a Practical Multi-Modal Context Further Advancements over the last three years that are yet to be presented Paper Objectives To extend the Truro Activity Based Parking Model (2006) To Include: Switching time of day Peak spreading Mode choice Response to parking tariffs and workplace parking levies On a small budget (Self funded academic research) 2

3 Standard WebTAG Demand Model Structure Composite Cost (p,j,t,m) Trip Frequency Composite Cost (p,j,t) Mode Choice (m) Composite Cost (p,j) Macro Time Period Choice (t) Composite Cost (p) Destination Choice (j) Assignment Costs Micro Time Period Choice (p) Assignment Connecting the Models Together The Logsum P car Ucar e U U car e e pt U car 1 car 2 Time Dist car U ASC IVT Cost Wait Walk pt pt 1 pt 2 pt 3 pt 4 pt 5 Interchange pt U U car pt Ln( e e ) = the Logsum or composite utility Represents the levels of accessibility across all alternatives 3

4 Modified Model Structure Simple Composite Cost (c,p,j,t,m) Trip Frequency Composite Cost (c,p,j,t) Mode Choice (m) Composite Cost (c,p,j) Macro Time Period Choice (t) Composite Cost (c,p) Destination Choice (j) Composite Cost (c) Micro Time Period Choice (p) Assignment Costs Parking Model (c) Assignment Trip Frequency Model 2 Modes (Car and Public Transport) Modified Model Structure Detailed Trip Frequency Mode Choice 5 Time Periods (Pre, AM, Inter, PM, Post) TP Choice Car PT 48 Destination zones 15 Time Intervals Pre = 1 AM = 6 Inter = 1 PM = 6 Post = 1 Dest Choice TI Choice Dest Choice Dest Choice Dest Choice Dest Choice TI Choice TI Choice TI Choice TI Choice 210 Car Park Choice PNR Pay and Display On Street Individual Car Park Assignment CP Car Parks Time Interval Highway Assignment CP CP CP CP CP CP CP CP CP CP CP CP CP CP Car Parks Skims by Time Interval 4

5 Running the Model Probabilities or Individuals Logit models predict the probability of making a particular choice based on the utility of all alternatives within the choice set These can be applied as probabilities or using microsimulation to make an actual choice Peak Spreading is based upon the Preferred Arrival Times (PAT) which is different for every individual Parking is based upon 1 car per space not proportions of cars This leads to the requirement for microsimulation to be used Fractional Probability Mode 1 (0.05) Destination 1 (0.15) Mode 2 (0.03) Mode 3 (0.07) Tour Destination 2 (0.75) Destination 3 (0.10) Mode 1 (0.15) Mode 2 (0.25) Mode 3 (0.35) Mode 1 (0.05) Mode 2 (0.02) Mode 3 (0.03) Transport planning, railways, research, software 5

6 Monte Carlo Simulation Uses a random number to make a choice from a cumulative probability distribution Example - Mode Choice Mode Utility Exp p( (Utility) Probability Cumulative Probability Bus Quality Bus Park & Ride Car Total Random Number Draw = 0.69 = Car ulative Probability Cum Bus Q Bus P&R Car Mode Microsimulation X Destination 1 (0.15) Tour Destination 2 X X Mode 1 (0.15) Mode 2 (0.25) Mode 3 X Destination 3 (0.10) Transport planning, railways, research, software 6

7 The Individual Journeys File Using microsimulation enables the construction of a journeys file as each decision is made The starting point is a set of individual journeys leaving a particular zone (TF Model) Modal Choice is added (Mode Choice Model) Time Period is added (Time Period Choice Model) Destination zone is added (Destination Choice Model) Socio Economic Variables can be inferred from other data sources The Top of the Demand Model Composite Cost (c,p,j,t,m) Trip Frequency Composite Cost (c,p,j,t) Mode Choice (m) Composite Cost (c,p,j) Macro Time Period Choice (t) Composite Cost (c,p) Destination Choice (j) This section of the model is not covered in this paper 7

8 The Bottom of the Demand Model - Simple Composite Cost (c) Micro Time Period Choice (p) Assignment Costs Parking Model (c) Assignment This part of the model is covered in the paper The Bottom of the Demand Model - Detailed 15 Time Intervals Pre = 1 AM = 6 Inter = 1 PM = 6 Post = 1 TI Choice TI Choice TI Choice TI Choice TI Choice 210 Car Park Choice PNR Pay and Display On Street Individual Car Park Assignment CP Car Parks Time Interval Highway Assignment CP CP CP CP CP CP CP CP CP CP CP CP CP CP Car Parks Skims by Time Interval 8

9 Highway Assignment Model Requirements Model the day as a continuum of different highway assignment models Disaggregate modelled time periods where peak spreading is an issue into 30 minute time intervals (AM Peak and PM Peak) Produce Pre Peak and Post Peak Time Periods Leads to 15 individual assignment models Need to ensure assignment models are consistent: Queues passed from one time period to the next Differences in demand/actual flow dealt with in subsequent time periods PASSQ Functions A Quasi Dynamic function in SATURN that enables the modelling of successive time periods The residual queues at the end of the first time period and the suppressed traffic are calculated from the input UFS file. The suppressed trips are then added as fixed link and turn flows in the second time period equal to the differences between demand and actual flows. In addition the residual queues per turn are converted into an effective fixed flow equal to Q/LTP which is added to the link entry flow for the purposes of the Assignment The residual queues from the previous time period are also taken into account in the delay calculations for the current time period for those specific turning movements If further time periods are to be modelled, the process is repeated using the previous output file as input 9

10 PASSQ Specification The models are automated using a batch file The following 15 time periods are modelled to 7.00 Pre Peak to 7.30 Pre AM Peak to 8.00 Pre AM Peak to 8.30 AM Peak to 9.00 AM Peak to 9.30 Post AM Peak to Post AM Peak to Inter Peak to Pre PM Peak to Pre PM Peak to PM Peak to PM Peak to Post PM Peak to Post PM Peak to Post Peak PASSQ Illustration Queues and Model 1 Suppressed Traffic Model 2 (UFS) Time and Distance Skims (UFS) Time and Distance Skims (UFS) Parking Model Parking Model 10

11 The Parking Model - Description Take trip destination building location, arrival time and activity duration 1 If building has its own car park with space available at this arrival time then goto 3 2 If not develop choice set of possible alternative car parks get in-vehicle time to car park from skim get walk time to building from mapping use activity duration to get parking cost logit model to get probabilities monte carlo to select one car park 3 Allocate to this car park for duration parked for each car park space, for each 15 minute interval, keep track of empty/full remove from empty parking stock Algorithm (1) Generate Population and Activity Patterns O-D Skims of Travel Time By TI Duration of Stay Distribution Distribution of Buildings (Staff Numbers) By Purpose Individual Trip Records From TI Choice Model Arrival Time at Car Park Duration of Stay (Monte Carlo) Building of Destination (Monte Carlo) Sort By Car Park Arrival Time Updated Tour File 11

12 Algorithm (2) Select a Car Park Tour File Does Building Have Its Own CP If No Derive Car Park Choice Set: Space at Arrival Time For Activities Duration Length of Stay Permitted Not Somebody Else's Car Park If Yes Derive Attributes Is there space in CP at CP Arrival Time for Required Duration of Stay Choose a Car Park and Car Park Space If Yes Position Car in CP Mark 1 Space in CP busy from Arrival Time for Duration of Stay Go To Next Record Algorithm (3) Derive Attributes Parking Inventory Car Park Space Availability Highway Skim Node to Node Parking Inventory Tariff Information Building Inventory CP Delay Functions Car IVT from O Zone to Nearest Node to Car Park Car IVT from Nearest Node to Car Park to Car Park Cost of Parking in Car Park For this Duration of Stay Walk Time From Car Park to Building Destination Current Delay at This Car Park 12

13 Diagram showing how these attributes are obtained Origin of Trip (HH) Destination Building IVT Skim Of Travel Time to Nearest Node to Car Park Walk From Car Park to Destination Building Average HW Speed - Nearest Node to Car Park Car Park Algorithm (4) Choose the Car Park and Car Park Space Output Logsums To TI Choice Model Choice Set Logit Model: Choice Probabilities for each car park Choice Coefficients Select Car Park and Car Park Space (Monte Carlo) Position Car in CP Mark 1 Space in CP busy from Arrival Time for Duration of Stay Go To Next Record 13

14 Model Structure Car Park Choice Calculate the Utility of choosing each individual car park U ( * IVT) ( * Cost) ( * Walk) cp Calculate the probability of choosing each individual car park P cpi ( ) Ucpi e e ( ) ln j Ucpj j e U cpj The value of is passed forward to the Time Interval Choice model and represents the cost of travelling to arrive at time including the composite costs across all car parks 210 individual car parks comprising office building car parks (PNR), on-street, pay and display car parks Time Interval Choice Model (Peak Spreading) Deals with micro time period choice where the time periods are disaggregated into shorter time intervals (in this case one 3 hour period into six 30 minute intervals) The choice of when to travel is dependant upon the Preferred Arrival Time (PAT) at the destination Ideally PAT s will be observed during data collection In this case they are derived from departure time distributions (Added to the Tour File) Travellers have certain constraints on their choice of when to travel: Workers must arrive at work before the work start time School children must arrive before the school start time Need to arrive before a certain time in order to get a parking space Etc. 14

15 From Small (1982) Model Structure Time Interval Choice U( ) ( ) SDE SDL dl Where: U ( ),,, The utility of arriving at time ( ) The utility of travel associated with arriving at time are coefficients And the terms SDE (Schedule Delay Early), SDL (Schedule Delay Late), and (dummy (0,1) for late arrival) are defined as SDE Max( PAT,0) SDL Max( PAT,0) d 1 if PAT, L 0 otherwise P d L i U i e e j U j Equilibrium Scheduling Theory, Hyman (1997) Development of Travel Times in an Equilibrium Schedule Model Travel Duration Constant Utility / / C2 C1 ta1 PAT1 PAT2 ta2 Arrival Time Source: Modelling Peak Spreading and Trip Re-timing: Hague Consulting Group 15

16 Trip Frequency Model 2 Modes (Car and Public Transport) The Model Structure Trip Frequency Mode Choice 5 Time Periods (Pre, AM, Inter, PM, Post) TP Choice Car PT 48 Destination zones 15 Time Intervals Pre = 1 AM = 6 Inter = 1 PM = 6 Post = 1 Dest Choice TI Choice Dest Choice Dest Choice Dest Choice Dest Choice TI Choice TI Choice TI Choice TI Choice 210 Car Park Choice PNR Pay and Display On Street Individual Car Park Assignment CP Car Parks Time Interval Highway Assignment CP CP CP CP CP CP CP CP CP CP CP CP CP CP Car Parks Skims by Time Interval Calibration statistics are presented for: Time Period Choice Models Time Interval Choice Model Parking Choice Model Model Calibration Statistics The calibration shows that the model represents observed data to a reasonable level of accuracy 16

17 Calibration Time Period Choice Model Calibration Time Interval Choice Model 17

18 Previous Calibration Parking Model Model Testing 1 Doubling congestion in the peak hours (8 9 and 17 18) Nb: Peak periods are 7 10 and 16 to 19 18

19 Forecast Peak Hours (8-9 and 17-18) Doubling of Congestion Peak periods don t change much Forecast Peak Hours (8-9 and 17-18) Doubling of Congestion Results Peak spreading Peak hour elasticity

20 Forecast Peak Period Doubling of Congestion Results Pay and display car park profile show people park slightly earlier (but not much) Buildings and on street car parks fill up earlier Model Testing 2 Workplace parking levy and expansion of the controlled parking zone (CPZ) i.e. 5 charge for all current free car parks 20

21 Forecast - 5 charge for all current non pay car parks Results Some time interval switching earlier in the am peak Forecast - 5 charge for all current non pay car parks Results People switch to pay and display 21

22 Conclusions Both parking and peak spreading can be incorporated into a compliant model structure To model parking effects you need to model peak spreading To model peak spreading you need to model parking Microsimulation using an Activity Based Approach offers significant advantages SATURN provides the capability for modelling the day as a continuum and providing consistent skims for this process Questions 22