Webinar: Development of a Pedestrian Demand Estimation Tool
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1 Portland State University PDXScholar TREC Webinar Series Transportation Research and Education Center (TREC) Webinar: Development of a Pedestrian Demand Estimation Tool Kelly Clifton Portland State University, kclifton@pdx.edu Let us know how access to this document benefits you. Follow this and additional works at: Part of the Transportation Commons, Transportation Engineering Commons, and the Urban Studies Commons Recommended Citation Clifton, Kelly, "Webinar: Development of a Pedestrian Demand Estimation Tool" (2016). TREC Webinar Series This Book is brought to you for free and open access. It has been accepted for inclusion in TREC Webinar Series by an authorized administrator of PDXScholar. For more information, please contact pdxscholar@pdx.edu.
2 Development of a Pedestrian Demand Estimation Tool Framework and Methods Kelly J Clifton, PhD
3 Outline Background Project, methods, zones, & data Pedestrian index of the environment (PIE) I: Trip generation II: Walk mode split III: Pedestrian destination choice Conclusions & future work Adapted from: 2
4 BACKGROUND
5 Background Why model pedestrian travel? plan for pedestrian investments & non-motorized facilities mode shifts greenhouse gas emissions health & safety new data 4
6 Background How do travel models estimate walking? Among 48 large MPOs in US: 38% did not estimate walking 33% estimated non-motorized (walking + bicycling) travel 29% estimated walking Lacking pedestrian built environment measures & small spatial units Trip-based model sequence 1. Generation 2. Distribution 3. Mode choice 4. Assignment Source: Singleton, P. A., & Clifton, K. J. (2013). Pedestrians in regional travel demand forecasting models: State-of-the-practice. 5
7 Background What are some opportunities? Walking behavior data improved travel surveys, pedestrian count data collection Built environment data archived spatial datasets, GIS processing Travel demand models smaller zones, complete networks, computer power Walking behavior research more knowledge and studies 6
8 PROJECT, METHODS, ZONES & DATA
9 Project overview Partnered with Metro: metropolitan planning organization for Portland, OR Two research projects Improve representation of pedestrian environment in current 4-step method 8
10 Current 4-step method TAZ = transportation analysis zone Trip Generation (TAZ) Trip Distribution or Destination Choice (TAZ) Pedestrian Trips Mode Choice (TAZ) Trip Assignment All Trips Pedestrian Trips Vehicular Trips 9
11 New MoPeD method I Trip Generation (PAZ) TAZ = transportation analysis zone PAZ = pedestrian analysis zone Walk Mode Split (PAZ) Trip Distribution or Destination Choice (TAZ) III Destination Choice (PAZ) Mode Choice (TAZ) Pedestrian Trips Trip Assignment All Trips Pedestrian Trips Vehicular Trips 10
12 MoPeD Contributions Operates at a smaller spatial scale, more relevant to pedestrians (PAZ) Utilizes spatially fine-grained archived information on the built environment Incorporates knowledge of influences on pedestrian travel behavior Designed to work with regional travel demand model or as standalone tool 11
13 Pedestrian analysis zones 264 feet = 80 m 1 minute walk Metro: ~2,000 TAZs ~1.5 million PAZs TAZs PAZs Home-based work trip productions 12
14 Travel survey data Oregon Household Activity Survey (OHAS) Household-based survey One-day travel diary Portland region dataset (2011) 6,100 households 13,400 people 56,000 trips 4,500 walk trips 8% walk mode share 13
15 PEDESTRIAN INDEX OF THE ENVIRONMENT (PIE) 14
16 Pedestrian environment Pedestrian Index of the Environment (PIE) score = calibrated (6 dimensions) People & job density Transit access Urban living infrastructure Block size Sidewalk extent Comfortable facilities ULI = Urban Living Infrastructure: pedestrian-friendly shopping and service destinations used in daily life. 15
17
18 Visualizing PIE 100 Downtown core Downtown 80 Major neighborhood centers Lloyd District 17
19 Visualizing PIE 70 Suburban downtowns Gresham 60 Residential inner-city neighborhoods Laurelhurst 18
20 Visualizing PIE 50 Suburban shopping malls Clackamas Town Center 40 Suburban neighborhoods/subdivisions Aloha 19
21 Visualizing PIE 30 Isolated business and light industry N. Marine Drive 20 Rural, undeveloped, forested Forest Park 20
22 I. TRIP GENERATION 21
23 Trip Generation I Trip Generation (PAZ) TAZ = transportation analysis zone PAZ = pedestrian analysis zone Walk Mode Split (PAZ) Trip Distribution or Destination Choice (TAZ) III Destination Choice (PAZ) Mode Choice (TAZ) Pedestrian Trips Trip Assignment All Trips Pedestrian Trips Vehicular Trips 22
24 Trip Generation Metro currently has 8 trip production models applied to ~2,000 TAZs: HBW Home-based work; HBshop Home-based shopping; HBrec Home-based recreation; HBoth Home-based other (excludes school and college); NHBW Non-home-based work; NHBNW Non-home-based non-work; HBcoll Home-based college; and HBsch Home-based school. After testing for scalability, we applied the same models to our pedestrian scale ~1.5M PAZs 23
25 24
26 II. WALK MODE SPLIT 25
27 Walk mode split I Trip Generation (PAZ) TAZ = transportation analysis zone PAZ = pedestrian analysis zone Walk Mode Split (PAZ) Trip Distribution or Destination Choice (TAZ) III Destination Choice (PAZ) Mode Choice (TAZ) Pedestrian Trips Trip Assignment All Trips Pedestrian Trips Vehicular Trips 26
28 II Walk mode split Walk Mode Split (PAZ) Vehicular Trips Pedestrian Trips Prob(walk) = f(traveler characteristics, PIE) Data: Method: Spatial unit: 2011 OHAS, Production trip ends, 90% sample binary logit model pedestrian analysis zone (PAZ) 27
29 II Walk mode split models Traveler characteristics: Built environment: Household size, income, age, # of workers, # children, # vehicles PIE 28
30 II Walk model results Traveler characteristics: + positively related to walking negatively related to walking number of children in HH age of household head HH vehicle ownership Ped. Environment: odds of choosing to walk + 10 points PIE 43% increase (HBW) 54% increase (HBNW) 67% increase (NHB) Pseudo R (HBNW) (NHB) 29
31 Mode Split Validation 1. Apply the final model equations to trips in the validation sample (10% of data) and calculate the walk probability for each trip; 2. Average the probabilities to get the predicted walk mode share of trip ends (called sample enumeration) Model HBW HBO NHB Observed Walk Mode Share 2.9% 9.4% 6.7% Predicted Walk Mode Share 3.0% 9.5% 8.6% 30
32 Walk model application 31
33 Walk mode split I Trip Generation (PAZ) TAZ = transportation analysis zone PAZ = pedestrian analysis zone Walk Mode Split (PAZ) Trip Distribution or Destination Choice (TAZ) III Destination Choice (PAZ) Mode Choice (TAZ) Pedestrian Trips Trip Assignment All Trips Pedestrian Trips Vehicular Trips 32
34 III. DESTINATION CHOICE 33
35 Destination choice I Trip Generation (PAZ) TAZ = transportation analysis zone PAZ = pedestrian analysis zone Walk Mode Split (PAZ) Trip Distribution or Destination Choice (TAZ) III Destination Choice (PAZ) Mode Choice (TAZ) Pedestrian Trips Trip Assignment All Trips Pedestrian Trips Vehicular Trips 34
36 III Destination choice Prob(dest.) = function of Data: network distance size / # of destinations pedestrian environment traveler characteristics 2011 OHAS Destination Choice (PAZ) Pedestrian Trips Method: Spatial unit: multinomial logit model super-pedestrian analysis zone Six trip types: home-based: work (HBW), shopping (HBS), recreation (HBR), & other (HBO); non-home-based: work (NHBW) and non-work (NHBNW) 35
37 Destination choice 36
38 III Destination Choice superpaz: a grid of 5 5 = 25 PAZs Choice set generation: Random sample of 10 superpazs within 3 miles 99% of OHAS walk trips < 3 miles (4.8 km) 37
39 III Destination Choice Key variables Impedance Size network distance btw. zones Additional variables employment by category, households Pedestrian supports PIE, parks Pedestrian barriers slope, freeway, industrial LUs Traveler attributes auto own., children 38
40 III Destination Choice Impedance Size + 1 mile of distance by auto own.: by children: by children: 2 # destinations minimum: maximum: odds of walking to destination 76 86% decrease (*) -62% (no), -74% (yes) (HBW) -78% (no), -83% (yes) (HBR) -78% (no), -90% (yes) (HBS) * Except for HBW, HBR, and HBS. odds of walking to destination 28 42% increase ( ) 4% increase (HBR) 88% increase (HBS) Except for HBR and HBS. 39
41 III Destination Choice Ped. supports odds of walking to destination + 10 points PIE: 16 34% increase (*) presence of park: 58% increase (HBR) Ped. Barriers * Except for HBS and HBR. odds of walking to destination + 1 mean slope: 14 35% decrease (2,3,4) presence of freeway: 64% decrease (2) + 1% industrial jobs: 33 82% decrease (1,2,3,4) 1 HBW, 2 HBS, 3 HBO, 4 NHBW. Pseudo R (HBR) (HBS) 40
42 III Destination Choice 41
43 III Destination Choice PIE = 75 PIE = 85 42
44 Destination choice Model Validation % Correct Destination 43
45 Destination Choice Model Validation Avg. Distance Walked 44
46 Destination Choice 45
47 CONCLUSIONS & FUTURE WORK 46
48 Conclusions Nests within current model but can be used alone Pedestrian scale analysis (PAZs) Pedestrian-relevant variables (PIE) One of the first studies to examine pedestrian destination choice in modeling framework Highlights policy relevant variables: distance, size, pedestrian supports & barriers 47
49 Future work Before application: Relate PIE more explicitly to policy changes Forecasting inputs Test method in other area(s)/regions Examine relationships in other contexts Assess PIE s transferability Provide agency guidance for implementation 48
50 Future work Research & Model Improvements: Trip Generation Multinomial Logit model Destination Choice Allocate from superpaz to PAZ level Explore non-linear effects & other interactions Route choices or potential pathways Need fundamental research to improve understanding 49
51 Questions? Project info & reports: Kelly J. Clifton, PhD Patrick A. Singleton Christopher Muhs Robert Schneider, PhD Portland State University DKS & Associates Univ. Wisconsin Milwaukee 50
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