Transportation Planning 2024 PDF

An Introduction to Transportation Planning Dr Subeh Chowdhury Department of Civil & Environmental Engineering The University of Auckland, Ext. 84116 Email: [email protected]...

An Introduction to Transportation Planning Dr Subeh Chowdhury Department of Civil & Environmental Engineering The University of Auckland, Ext. 84116 Email: [email protected] 1  Example of Transportation Planning The Puhoi to Warkworth four-lane Northern Motorway (SH1) extension of 18.5km cost $877 million. Benefits are: Improved safety and connectivity Improved journey time reliability Easier freight movement Boosting the i economic potential of the Northland region. 2 Questions How do we decide which project to fund? Which project will make the most of taxpayers’ contributions? How do we improve New Zealand’s economy? How do we stay internationally competitive? 3 Learning Objectives Describe the data collection process for a transportation planning project. Describe equity issues related to transportation planning process. Explain the factors which influence each step of the 4-step model. Analyse problems using the 4-step model. 4 Transportation Planning: Overview Transportation planning provides the information, tools, and public involvement needed for improving transportation system performance. whytransportation a Transportation planning is a continuous process that requires monitoring of the system’s performance and condition. Ans Toshowthattheres is thatrelationship betweenlanduse and transportation planning Transportation planning affects, Policies gainintoft Choices among alternative strategies transport public or motorway sector Priorities Funding allocations 5 Transportation Planning Transportation planning forms the base of how a city runs. It determines which modes are priorities, how wide the transport network will be, and who benefits the most (equity question to be discussed later). It is the core of a transportation system. A transport system can break or make an economy. 6 Highway to “no where” Devised by Transport Planner Robert Moses, it destroyed a thriving black community in Baltimore. They city used to have good walkability to parks, markets. The story was to “improve connectivity and reduce slums’. But this was not the case. The “ghettos” were thriving communities. Many houses and families were evicted and told their homes will be destroyed to make room for the new highways.  Amsterdam Transport Network Two different systems. Why? Singapore Topography is a key driver of how a transport system is developed. History is another factor. 8 Learning Objective 1 Describe the data collection process for a transportation planning project. 9 Typical model for development The best model for development is to take into account the: o Needs of the community; o Making the economy globally competitive; o Develop a multimodal infrastructure that is efficient and sustainable. Image reference: M.D. Meyer (2016). Transportation Planning 10 Handbook. John Wiley & Sons, page 77. Let’s look at why For example: To have a competitive economy, the transport sector needs to support the labour market. What does this mean? o Essentially focus resources on those who can take part in the labour force. o So what happens to those who are not commuters? 11 Hierarchical structure Transportation Policy (Administrative/Political) Transportation Study (Planners, Engineers) Transportation Design (Design Engineers) 12 Transportation Planning Process Transportation Studies  Transportation Survey Identify present system inefficiency development Derive travel relationships mode choice route usage  Transportation Modelling To predict future transport requirements To evaluate alternative proposals 13 Transportation Survey Household data (trips per household by size, vehicle ownership) Transport Network data (crash rates, AADT) Mode share (private, public transport, cycling, walking) Land use and development Employment hubs Education Hubs 14 Image reference: M. Rogers and B. Enright, (2016). Highway 15 Engineering, John Wiley & Sons Inc. Chapter 1 and 2. Transportation Planning: Steps 1) Planning = Looking into future 2) Investigate current situation 3) Investigate possible decisions Do nothing Do something (alternatives) 4) Rating alternatives – Depending on our goals and objectives 5) Estimate future requirement 6) Presenting useful information to decision makers such as government agencies. 16  The Study Area Cordon line: The boundary around the study area which trip Screen line: movements are Counts taken at required. all streets- intersecting an imaginary line. then FFints forcollection Image reference: M. Rogers and B. Enright, (2016). Highway 17 Engineering, John Wiley & Sons Inc. Chapter 1 and 2.  Cordon and Screen lines Image reference: M.D. Meyer (2016). Transportation 18 Planning Handbook. John Wiley & Sons, page 41.  Subdividing the Study Area Subdividing the Study Area for Forecasting Analysis Units (Zones)Traffic AnalysisZone TAZ Urban Activities Land use activities Office and factory floor areas + employment figures for commercial/industrial activity Census info and housing density will indicate population size Socio-economic information (income level) Image reference: M. Rogers and B. Enright, (2016). Highway 19 Engineering, John Wiley & Sons Inc. Chapter 1 and 2.  Transportation System Links and Nodes “Spider Network” – a simplified grid in which zone centroids are joined to adjacent ones by links. Nodes are points where the links intersect. This is used in Trip Assignment (to be discussed later). Image reference: M. Rogers and B. Enright, (2016). Highway 20 Engineering, John Wiley & Sons Inc. Chapter 1 and 2.  Trip Characteristics Trip purpose: this trip characteristics is one of the most important factors. It influences data collection, the 4-step model, how people behave. Travel time: If cost is not a factor, most mode choices are decided by the time it takes by each mode to reach a destination. Personal safety: For women, this is the most important characteristics for any travel decisions. Time of day: Image reference: M.D. Meyer (2016). Transportation 21 Planning Handbook. John Wiley & Sons, page 33. Types of data collection methods Origin-Destination Survey originanddestination Registration plate method Objective is to match vehicles at their origin and Post card method destination. Roadside interview method Home interview method (Census): most complete and accurate information expensive, time consuming most expensive 22  Travel Information (Existing) Origin-Destination (O-D) Data Where trips come from Where trips go to To determine travel patterns and inputs By which mode for demand models. For what purpose Characteristics of trip makers Socio-economic, income Activities at origin and destination of the trip 23 Image from iStock, Creative Commons CC0 Survey Process Being responsible for a survey requires the transportation engineer to be very organised and precise. Surveyors will typically follow instructions and are not responsible for any issues in the data collection. 24 Travel Information Stratification of Trip Purposes To establish trip patterns by purpose. Home-Based-Work Home-Based-Other Non-Home-Based Internal-External : Trips with either their origin or destination outside the study area. Through Trips: Trips that pass through the study area. Neither origin nor destination is in the study area. 25 Learning Objective 2 Describe equity issues related to transportation planning process. 26 Transportation Planning Process Decision Making Process Economic Assessment (Cost Benefit Analysis) – mainly concerned with travel time and cost. Environmental Assessment (air, water, noise quality, ecology, visual intrusion, impact on local communities, etc.) Public consultation (public hearing, public inquiry) 27 Top-Down/Bottom-Up Approach TOP Where should public consultation take place, at the bottom or closer to the top of the decision making process? Down 28  200 3 marks in test or exam Other equity issues Prioritization of study area oLocation oCordon line selection Data collection time period oWhen both peak and off peak hours oWhere oHow Purpose of trips oWhen oWhere 29 Learning Objective 3 & 4 Explain the factors which influence each step of the 4-step model. Analyse problems using the 4-step model. 30  Transportation Modelling Standard Four-Step Process Step 1: Trip Generation forecasts the number of trips generated or attracted Step 2: Trip Distribution determines where the trips will go Step 3: Modal Split predicts how the trips will be divided among the available modes of travel Step 4: Trip/Traffic Assignment predicts the routes that the trips will take 31 Image reference: M. Rogers and B. Enright, (2016). Highway Engineering, John Wiley & Sons Inc. Chapter 1 and 2.  Transportation Modelling Mathematically: Step 1: Trip generation Oi(t) = f(Li(t)) Dj(t) = f(Lj(t)) focusinTP Step 2: Trip distribution Tij(t) = f(Oi(t) Dj(t)) b.tnainto T.fi cases Step 3: Modal split emoa Tijm(t) = f(Tij(t)) EEim EE modescavvehicle Step 4: Traffic assignment bus whichroutethat triphast aken Tij mr(t) = f(Tijm(t)) 32 Image reference: M. Rogers and B. Enright, (2016). Highway Engineering, John Wiley & Sons Inc. Chapter 1 and 2.  Trip generation models predict the total number of trips produced by (expected to leave) and attracted to (expected to arrive at) each zone. Stage 1 - Trip Productions of trips Generation Attractions of trips What variables influence the number of productions and attractions to/from a zone? 33 Trip Generation Trip generation is the analytical process that provides the relationship between urban activity and travel. The number of trips to and from activities in an area are related to land use and socioeconomic characteristics, and used to relate the number of trip ends to the intensity of land use activity. Depending on the design of the overall study process, trip generation models can be derived for person or vehicle movements, by trip purpose and time of day. 34 Trip Generation Trip generation models accept land use characteristics as an input to produce zonal trip ends as an output. Different uses of land produce different trip generation characteristics. Mathematically, Oi(t)= f(Li(t)) Dj(t)= f(Lj(t)) where: Oi = number of trips originating in zone i Dj = number of trips attracted to zone j Li, Lj = measures of land use intensity in zones i and j t = a particular point in time 35  Land use Measure of land use Number classification activity of trip ends Residential Li, number of persons Oi living in zone i Oi Li, number of workers living in zone i Industrial Lj, number of jobs in Dj Common zone j Dj Origins and Lj, industrial area in sq. km. in zone j Destinations Commercial Lj, number of parking Dj space in zone j Dj Lj, office floor area in sq. m. in zone j Recreational Lj, number of hotel Dj rooms in zone j Dj Lj, seating capacity in zone j 36  i Trip Generation Estimation Methods for Trip Generation 1. Expansion factor: This method only takes into account trips; other factors such as car ownership and socio-economic activities are excluded. common 2. Regression analysis: This method measures the separate influence of each factor in association with other factors. 3. Category analysis: This method is based on the assumption that trip generation rates for different categories of households will remain constant in the future. 37 Image reference: M. Rogers and B. Enright, (2016). Highway Engineering, John Wiley & Sons Inc. Chapter 1 and 2. Example - Expansion Factor Currently (at t =0), a suburb “A” has 220 hectares of residential area that produces 99,000 trip ends. In the design year (at t = 1) it is expected that the residential area will increase to 310 hectares. Find the total number of trip ends in the design year. Solution: Step 1:Expansion factor = 99,000/220 = 450 Step 2:Number of trip ends in design year= 450 x 310 = 139,500 trips 38 Regression Analysis Regression is a technique for describing the relationship between the dependent variable, Y, and independent variable(s), xi, where i = 1 to n. Y = f (x1, x2 …..) Regression can be single or multiple variables. In trip generation models, the dependent variables are Oi and Dj and the independent variables are L’s ( Li or Lj). 39 Examples of Regression Analysis Oi = 12.5 + 2.105 Ci + 0.88 Wi where: Oi = number of trips originated from zone i Ci = number of automobiles owned by households in zone i Wi = number of workers in zone i Dj = 218 + 17.24 X1 + 3.255 X2 where: Dj = number of trips attracted to zone j X1 = 1000 sq. m of retail floor areas X2 = 1000 sq. m. of service and office floor areas Note: Regression coefficients are estimated from travel survey data. Why can this be an issue? 40  Category Analysis In developing such regression equations, assumptions of linearity and that the variables are independent, in addition to biases or inaccuracies in the survey data have resulted in support for Category Analysis. Category Analysis has the household (or person) as its basis and assumes that the volume of trips generated depends mainly on household characteristics. For example, in regression analysis you can consider trip characteristics too (travel time, travel cost…) 41 Image reference: M. Rogers and B. Enright, (2016). Highway Engineering, John Wiley & Sons Inc. Chapter 1 and 2. Category Analysis A typical Category Analysis table includes daily trip rates per household category. Information needed for the zone under consideration is in the form of another Category Analysis table which identifies the number of households from within the zone in each category. The trips generated can then be calculated. 42 Category Analysis This method is also known as “cross classification analysis”. In this method, the first step is to decide what factors will be used as a basis for estimation of the number of trips made per household. The range of these socio-economic characteristics are then divided into a set of classes, for example: number of cars owned: 0, 1, 2, 3+ family size: 2, 3, 4, 5, 6+ income levels: 500-1000, 1000-2000, 2000-3000 etc. 43 Category Analysis Then data on the actual number of trips made by each household are gathered, along with all the other information required to estimate trip generation. Each household is then included in the appropriate classification cell and the mean trip rate is computed for each classification cell. This mean trip rate is then used to estimate the number of trips made by households in the future. 44 Example – Category Analysis Table 1: Number of households and total trips made, categorized by household size and auto-ownership level (City “A” in 2010) Auto Ownership Family 0 1 2+ # of # of Trips # of # of Trips # of # of Trips Size Households Households Households 1 925 1,098 1,872 4,821 121 206 2 1,471 2,105 1,934 6,129 692 1,501 3 1,268 1,850 3,071 13,989 4,178 19,782 4+ 745 1,509 4,181 18,419 4,967 25,106 45 Example – Category Analysis Table 2: Forecasted number of households in zone A, categorized by household size and auto-ownership level (City “A” in 2025) Auto Ownership Family Size 0 1 2+ 1 24 42 8 2 10 51 107 3 11 31 158 4+ 3 17 309 Find the future trips from this zone in year 2025. 46 Example – Category Analysis Solution: Find the mean household trip rates first, from Table 1. Auto Ownership Family Size 0 1 2+ 1 1.19 2.57 1.7 2 1.43 3.16 2.17 3 1.45 4.55 4.74 4+ 2.02 4.4 5.05 1098/925= 1.187 =1.19 47 Example – Category Analysis Forecasted number of trips from Zone A (year 2025) Auto Ownership Total Family Size 0 1 2+ 1 29 108 14 151 2 14 161 232 407 3 16 141 749 906 4+ 6 75 1,561 1,642 Total 65 485 2,556 3,106 1.19*24 = 28.48=29 48 Stage 2 - Trip Distribution For trips produced by the zone in question, the trip distribution model determines the individual zones where each of these will end. P For trips ending in the zone under examination, the individual zone within which each trip originated is determined. A The model thus predicts zone-to-zone trip interchanges. Objective: Estimation of the target-year trip volumes Tij that interchange between all pairs of zones i and j, where i is the trip-producing zone and j is the trip-attracting zone of the pair. 49 Trip Distribution After the trip generation process, we know the number of trips generated in each zone Oi and attracted to each zone Dj. In this step, we want to determine the interzonal / intrazonal distribution of these trips. For each zone, a determination is made of the zones to which these produced trips will be attracted based on the attractiveness of the potential destination points and the costs or impedance of travel (i.e. how the trips produced in a zone are distributed amongst all of the other zones). 34 50 Trip Distribution All trip-attracting zones of the pair j in the region will compete with each other to attract trips produced by each zone i. More trips will be attracted by zones that have a higher level of “attractiveness” (less generalised cost – reduced travel time, low travel cost, comfortable, safe, there is enough information) Other factors that might affect the choice of j include impedance, e.g. distance. Production and attraction inputs are obtained from trip generation (Stage 1) phase. 51  Trip Distribution Estimates of the target-year interzonal impedance are obtained from the specification of the transportation plan(s). Interzonal trips (Tij) i ≠ j; intrazonal trips i = j Tij, i = 1, 2, ….,n Base year trip matrix and horizon year trip matrix Trip production and attraction conditions n n T  P j 1 ij i T  A i 1 ij j 52 Image reference: M. Rogers and B. Enright, (2016). Highway Engineering, John Wiley & Sons Inc. Chapter 1 and 2. Trip Distribution The end product is an origin destination trip matrix. Zone of Origin Zone of destination (Attraction) (Production) 1 2 3 Total trip production from zone i 1 T11 T12 T13 P1 2 T21 T22 T23 P2 3 T31 T32 T33 P3 Total trip attraction A1 A2 A3 to zone j 53  Gravity Model The Gravity Model is the most popular model and derives its name and basic premise from Isaac Newton’s law of gravity: Pi Aj Aj A j Pi Pi Tij  t n tj n or Tij  tijn i t n ij ij ij where: Tij is the number of (predicted) trips between zones i and j. Pi is the production for zone i (for that purpose). Aj is the attraction for zone j (related activity for purpose). tij is the travel time (or cost or distance) between zones i and j. n is the empirically derived travel time exponent. 54 Image reference: M. Rogers and B. Enright, (2016). Highway Engineering, John Wiley & Sons Inc. Chapter 1 and 2. Attraction/Production Pi Aj Aj Tij  t n t j n ij ij A1 Pi A2 A3 55 Attraction/Production A j Pi Pi Tij  tijn i t n ij P1 Aj P3 P2 56 Gravity Model Exponents Example attraction measure and travel time exponents of gravity model Purpose of trip Exponent Work 0.5 Social 3 increase Asvalue the Shopping 2-3 theimportance of Business 2 tripreduces Recreation 2 Other 2 exponent the higher it shows more likelyyour the less making the to be frequent going a trip on that or 57 Example – Singly Constrained Origin zone (zone 1): 1,000 families 1,000 automobiles 100 shopping trips per day Pi 1 Destination zones: Zone Travel Activity (shopping time area, m2) 2 5 10,000 Atrium 3 20 40,000 4 10 20,000 58 Diagram of example 2 10,000 m2 5 100 shopping 20 trips 1 3 40,000 m2 10 4 20,000 m2 59 Solution – Singly Constrained  10,000   5 2  = 57 trips T12  100   10,000 40,000 20,000     5 2 20 2 10  2  40,000  = 14 trips  20 2  T13  100   10,000 40,000 20,000     5 2 20 2 10  2  20,000  = 29 trips  10 2  T  100  14 10,000 40,000 20,000       5 2 20 2 10  2 60 Problem 1 - Singly Constrained Gravity Model Shown is a hypothetical (and over- 1 C 3 simplified) zone and network arrangement for a residential area. 15 10 There is one residential zone (R) 6 and three shopping centres nearby 5 5 (A, B, C). 1 2 8 5 1 R 1 A 2 5 2 5 3 3 1 1 B 61 Problem 1 (contd) Assuming there are 1000 shopping trips made per day from the residential zone to the shopping centres (see table for retail area), determine the number to each centre based on a travel time exponent of 1.0. Re-evaluate the result based on a travel time exponent of 2.0. Shopping Centres Area A 3000 sq m B 1000 sq m C 6000 sq m 62 Stage 3 - Modal Split The modal split model determines the proportion, and therefore number of trips undertaken by each of the different modes. The split is dependent on the characteristics of the trip, traveller and transport system. Mode choice can be assumed to have its basis in the micro-economic concept of utility maximisation. The proportion carried by each mode is in relation to its competitors. We therefore need to develop an expression for the utility provided by any one of a number of mode options. 63 Modal Split U m   0    j zmj 64 Modal Split The probability of a commuter choosing mode m is represented by the following multi-nominal logit choice model: eU m Pm  e Uj where, j Pm = probability that mode m is chosen Um = index over all modes included in chosen set A simplified version for two modes is: P  1 A 1  e (U B U A ) 65 Stage 4 - Traffic Assignment A procedure to predict the paths to be taken by each trip. Output includes the following: the path that all trips will take the traffic flow on each roadway the number of passengers on each transit route 66 Traffic Assignment The traffic assignment model assigns precise quantities of traffic flow to specific routes within each of the zones based on these assumptions: Trip makers choose a route connecting their origin and destination on the basis of which one gives the shortest travel time Trip makers know the travel times on all available routes between the origin and destination Given the preceding assumptions, trip makers will select a route that minimises their travel time between origin and destination. 67  transports All or Nothing “All-or-Nothing” Assignment Simplest model assumes a linear relationship between travel time and speed (speed limit used) on the assumption that free-flow conditions exist. Trips are then assigned to the route of minimum time using the “all- or-nothing” algorithm as travel time is assumed to be independent of traffic volume. 68 Example - “All-or-Nothing” “All-or-Nothing” (AON) assignment does not take into account congestion effect all trips assigned to the shortest path at free flow condition Demand AON Assignment Tij = 2 C1 = 10 xi – link flows 0 C2 = 1 x1 = 0, C1 = 10 i j x2 = 2, C2 = 1 2 x3 = 0, C3 = 5 C3 = 5 0 Ci – Generalised cost function 69 Problem 2 - The AON method of traffic assignment The highway network for a four-zone network is shown below along with the interzonal travel times. The average daily trip interchanges between each of the zones is given in the table below. Using the AON algorithm calculate the traffic flows on each link of the network. 5 min Origin Destination zone 1 2 zone 1 2 3 4 1 - 250 100 125 10 min 5 min 2 300 - 275 200 3 150 325 - 100 3 4 4 200 150 50 - 15 min 70 Example - User Equilibrium In this simple example, travel time increases as traffic flow increases – travel time is volume dependent. UE Assignment Demand C1 = 10 + 2x1 xi – link flows Tij = 2 0 C2 = 1 + 5x2 x1 = 0, C1 = 10 in i j x2 = 1, C2 = 6 not 1 x3 = 1, C3 = 6 test C3 = 5 + x3 1 Ci – Generalised cost function 71 Applications The output of the four-stage transport model shows: the paths that all trips will take the number of cars on each roadway the number of passengers on each transit route Planners can estimate the effect of policies and programs on travel demand identify various impacts of the system on the urban area, such as energy use, pollution and accidents evaluate alternative methods of supply 72