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Transportation Planning and Traffic Estimation

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  1. Transportation Planningand Traffic Estimation CE 453 Lecture 5

  2. Objectives 1. Identify highway system components 2. Define transportation planning 3. Recall the transportation planning process and its design purposes 4. Identify the four steps of transportation demand modeling and describe modeling basics. 5. Explain how transportation planning and modeling process results are used in highway design.

  3. Highway System Components 1. Vehicle 2. Driver (and peds./bikes) 3. Roadway 4. Consider characteristics, capabilities, and interrelationships in design Start with demand needs (number of lanes?)

  4. Transportation Planning (one definition) Activities that: 1. Collect information on performance 2. Identify existing and forecast future system performance levels 3. Identify solutions Focus: meet existing and forecast travel demand

  5. Where does planning fit in?

  6. Transportation Planning in Highway Design 1. identify deficiencies in system 2. identify and evaluate alternative alignment impacts on system 3. predict volumes for alternatives • in urban areas … model? … smaller cities may not need (few options) • in rural areas … use statewide model if available … else: see lab 3-type approach (note Iowa is developing a statewide model)

  7. Truck Traffic

  8. Planning at 3 levels • State … STIP Statewide Transportation Improvement Program (list of projects) • Regional … MPO Metropolitan Planning Organization (>50,000 pop.), 25 year long range plan and TIP (states now also do LRP) • Local …project identification and prioritization

  9. Four Steps of Conventional Transportation Modeling 1. Trip Generation 2. Trip Distribution 3. Mode Split 4. Trip Assignment

  10. Study Area • Clearly define the area under consideration • Where does one entity end? • May be defined by county boundaries, jurisdiction, town centers

  11. Study Area • May be regional • Metropolitan area – Des Moines including suburbs, Ankeny, etc. • Overall impact to major street/highway network • Local – e.g., impact of trips to new Ames mall • Impact on local street/highway system • Impact on intersections • Need for turning lane or new signal – can a model do this level of detail?

  12. Study Area • Links and nodes • Simple representation of the geometry of the transportation systems (usually major roads or transportation routes) • Links: sections of roadway (or railway) • Nodes: intersection of 2+ links • Centroids: center of TAZs • Centroid connectors: centroid to roadway network where trips load onto the network

  13. Travel Analysis Zones (TAZs) • Homogenous urban activities (generate same types of trips) • Residential • Commercial • Industrial • May be as small as one city block or as large as 10 sq. miles • Natural boundaries --- major roads, rivers, airport boundaries • Sized so only 10-15% of trips are intrazonal

  14. www.sanbag.ca.gov/ planning/subr_ctp_taz.html

  15. Four Steps of Conventional Transportation Modeling • Divide study area into study zones • 4 steps • Trip Generation • -- decision to travel for a specific purpose (eat lunch) • Trip Distribution • -- choice of destination (a particular restaurant? The nearest restaurant?) • Mode Choice • -- choice of travel mode (by bike) • Network Assignment • -- choice of route or path (Elwood to Lincoln to US 69)

  16. Trip Generation Model Step #1…

  17. Trip Generation • Calculate number of trips generated in each zone • 500 Households each making 2 morning trips to work (avg. trip ends ~ 10/day!) • Worker leaving job for lunch • Calculate number of trips attracted to each zone • Industrial center attracting 500 workers • McDonalds attracting 200 lunch trips

  18. Trip Generation • Number of trips that begin from or end in each TAZ • Trips for a “typical” day • Trips are produced or attracted • # of trips is a function of: • TAZs land use activities • Socioeconomic characteristics of TAZ population

  19. Trip Generation Caliper Corp. ModelManager 2000™

  20. Trip Generation • 3 variables related to the factors that influence trip production and attraction (measurable variables) • Density of land use affects production & attraction • Number of dwellings, employees, etc. per unit of land • Higher density usually = more trips • Social and socioeconomic characters of users influence production • Average family income • Education • Car ownership • Location • Traffic congestion • Environmental conditions

  21. Trip Generation • Trip purpose • Zonal trip making estimated separately by trip purpose • School trips • Work trips • Shopping trips • Recreational trips • Travel behavior depends on trip purpose • School & work trips are regular (time of day) • Recreational trips highly irregular

  22. Trip Generation • Forecast # of trips that produced or attracted by each TAZ for a “typical” day • Usually focuses on Monday - Friday • # of trips is forecast as a function of other variables • Attraction • Number and types of retail facilities • Number of employees • Land use • Production • Car ownership • Income • Population (employment characteristics)

  23. Trip Purpose • Trips are estimated by purpose (categories) • Work • School • Shopping • Social or recreational • Others (medical) • Travel behavior of trip-makers depends somewhat on trip purpose • Work trips • regular • Often during peak periods • Usually same origin/destination • School trips • Regular • Same origin/destination • Shopping recreational • Highly variable by origin and destination, number, and time of day

  24. Household Based • Trips based on “households” rather than individual • Individual too complex • Theory assumes households with similar characteristics have similar trip making characteristics • However • Concept of what constitutes a “household” (i.e. 2-parent family, kids, hamster) has changed dramatically • Domestic partnerships • Extended family arrangements • Single parents • Singles • roommates

  25. Trip Generation Analysis • 3 techniques • Cross-classification • Covered in 355 • Multiple regression analysis • Mathematical equation that describes trips as a function of another variable • Similar in theory to trip rate • Won’t go into • Trip-rate analysis models • Average trip-production or trip-attraction rates for specific types of producers and attractors • More suited to trip attractions

  26. Trip attractions

  27. Example: Trip-rate analysis models For 100 employees in a retail shopping center, calculate the total number of trips Home-based work (HBW) = 100 employees x 1.7 trips/employee = 170 Home-based Other (HBO) = 100 employees x 10 trips/employee = 1,000 Non-home-based (NHB) = 100 employees x 5 trips/employee = 500 Total = 170 + 1000 + 500 = 1,670 daily trips

  28. Trip Distribution Model Step #2…

  29. Trip Distribution • Predicts where trips go from each TAZ • Determines trips between pairs of zones • Tij: trips from TAZ i going to TAZ j • Function of attractiveness of TAZ j • Size of TAZ j • Distance to TAZ j • If 2 malls are similar (in the same trip purpose), travelers will tend to go to closest • Different methods but gravity model is most popular

  30. Trip Distribution • Determines trips between pairs of zones • Tij: trips from TAZ i going to TAZ j • Function of attractiveness of TAZ j • Size of TAZ j • Distance to TAZ j • If 2 malls are similar, travelers will tend to go to closest • Different methods but gravity model is most popular

  31. Trip Distribution Caliper Corp. Maricopa County

  32. Gravity Model Tij = PiAjFijKijΣ AjFijKij Qij = total trips from i to j Pi = total number of trips produced in zone i, from trip generation Aj = number of trips attracted to zone j, from trip generation Fij = impedance (usually inverse of travel time), calculated Kij = socioeconomic adjustment factor for pair ij

  33. Mode Choice Model Step #3…

  34. Mode Choice • In most situations, a traveler has a choice of modes • Transit, walk, bike, carpool, motorcycle, drive alone • Mode choice/mode split determines # of trips between zones made by auto or other mode, usually transit

  35. Characteristics Influencing Mode Choice • Availability of parking • Income • Availability of transit • Auto ownership • Type of trip • Work trip more likely transit • Special trip – trip to airport or baseball stadium served by transit • Shopping, recreational trips by auto • Stage in life • Old and young are more likely to be transit dependent

  36. Characteristics Influencing Mode Choice • Cost • Parking costs, gas prices, maintenance? • Transit fare • Safety • Time • Transit usually more time consuming (not in NYC or DC …) • Image • In some areas perception is that only poor ride transit • In others (NY) everyone rides transit

  37. Mode Choice Modeling • A numerical method to describe how people choose among competing alternatives (don’t confuse model and modal) • Highly dependent on characteristics of region • Model may be separated by trip purposes

  38. Utility and Disutility Functions • Utility function: measures satisfaction derived from choices • Disutility function: represents generalized costs of each choice • Usually expressed as the linear weighted sum of the independent variables of their transformation U = a0 + a1X1 + a2X2 + ….. + arXr U: utility derived from choice Xr: attributes ar: model parameters

  39. Logit Models • Calculates the probability of selecting a particular mode p(K) = ____eUk__  eUk p: probability of selecting mode k

  40. Logit Model Example 1 Utility functions for auto and transit U = ak– 0.35t1 – 0.08t2 – 0.005c ak= mode specific variable t1 = total travel time (minutes) t2 = waiting time (minutes) c = cost (cents) Do you agree with the relative magnitude of the time parameters? Is there double counting/colinearity?

  41. Logit Model Example 1 (cont) Travel characteristics between two zones Do you agree with the relative magnitude of the mode specific parameters? How much effect does cost have? Uauto = -0.46 – 0.35(20) – 0.08(8) – 0.005(320) = -9.70 Utransit = -0.07 – 0.35(30) – 0.08(6) – 0.005(100) = -11.55

  42. Logit Model Example 1 (cont) Uauto = -9.70 Utransit = -11.55 Logit Model: p(auto) = ___eUa __ = _____e-9.70____ = 0.86 eUa + eUt e-9.70 + e-11.55 p(transit) = ___eUt __ = _____e-11.55____ = 0.14 eUa + eUt e-9.70 + e-11.55

  43. Logit Model Example 2 • The city decides to spend money to create and improve bike trails so that biking becomes a viable option, what percent of the trips will be by bike? • Assume: • A bike trip is similar to a transit trip • A bike trip takes 5 minutes more than a transit trip but with no waiting time • After the initial purchase of the bike, the trip is “free”

  44. Logit Model Example 2 (cont) Travel characteristics between two zones Uauto = -0.46 – 0.35(20) – 0.08(8) – 0.005(320) = -9.70 Utransit = -0.07 – 0.35(30) – 0.08(6) – 0.005(100) = -11.55 Ubike = -0.07 – 0.35(35) – 0.08(0) – 0.005(0) = -12.32

  45. Logit Model Example 2 (cont) Uauto = -9.70, Utransit = -11.55, Ubike = -12.32 Logit Model: p(auto) = _____eUa ____ = _______e-9.70 ______ = 0.81 eUa + eUt +eUb e-9.70 + e-11.55 + e-12.32 p(transit) = _____eUt__ __ = ______e-11.55______ = 0.13 eUa + eUt +eUb e-9.70 + e-11.55 + e-12.32 p(bike) = _____eUt__ __ = ________e-11.55______ = 0.06 eUa + eUt +eUb e-9.70 + e-11.55 + e-12.32 Notice that auto lost share even though its “utility” stayed the same

  46. Model Step #4… Traffic Assignment (Route Choice) Caliper Corp.