Third International Workshop on \"Geographical Analysis, Urban Modeling, Spatial Statistics\"

1. New cycle-lane project: a participative approach M assimiliano PETRI 1 Marco ROTONDA 1 from june 30th … ….. to july 3rd -Perugia- (Italy) 1 LISTA – Laboratory of Territorial and Environmental Systems Engineering 1 University of Pisa , Department of Civil Engineering

2. Topics 1 – The decisional problem 2 – Italian urban mobility 3 – The applied methodology - Environmental data acquisition - On line survey (Web application) elaboration - Decision Tree algorithm application - Extracted rules analysis

3. Pisa: re-project urban cycle-lines Possible streets to locate cycle-lines The project start to be a real decision support system for Pisa municipality that needs to understand how to re-project urban cycle-line and how to link actual lines with neighbouring tourist areas (a thermal center in S.Giuliano and some seaside resorts in Marina di Pisa).

4. Italian Urban Mobility (Cities < 100.000 inhabitants) Analysis of travel demand (Italian transport research centre “Audiomob”) Employment Status (Source: Isfort, Audimob,2006) 14-29 30-45 46-64 >65 Years Worker Unemployed Housewife Student Retired Age Groups

5. Italian Urban Mobility (Cities < 100.000 inhabitants) Analysis of transport means vs time band (Source: Isfort, Audimob,2006) From 6.00 to 11.00 From 11.01 to 14.00 From 14.01 to 18.00 After 18.00

6. Urban Mobility Research “ Audiomed” research centre has been undertaken, since 2000, the study of individual preferences and lifestyles and it identified in the daily habits a very important explanation in mobility choice and a justification of the observed sub optimal but repeated daily behaviours.

7. Urban Mobility Research Processing data referenced in space and time is possible to identify behavioural patterns A first attempt: GeoPKDD (Geographic Privacy-aware Knowledge Discovery and Delivery) mobile phones space-time trajectories

8. Urban Mobility Research Some Problems The agents’ trajectories analysis isn’t able to provide exact information on visited places, and the citizens’ preferences are not assessed. Similar studies, aimed at identifying the preferential movement lines within the city, are not generally able to grasp if the chosen path is the preferred one or it is bound by urban context in which it occurs.

9. Metodology Design a methodology that includes space-time information and individual preferences On line (or with PDA) questionnairies collection with preferences and scenarios Individual behavioural Rules extraction All collected information were processed through Gis and datamining tools. New cycle-lane project PREPROCESSING Spatio-temporal data (urban environment) PARTECIPATORY ANALISYS Interactive WEB – GIS PROCESSING Input and survey data organizing DATAMINING Analisi GIS GIS

10. New cycle-lane project Urban Environmental Data Existing cycle-lines Existing cycle-services Guarded byke places Byke racks Bike sale Bike rent Pedestrian area

11. New cycle-lane project A Partecipative Approach METHDOS TO LISTENING Active Listening Brainstorming Walk Neighbourhood Survey Outreach Focus Group METHDOS TO SHARE DECISIONS Open Space Tecnology Planning for real SWOT EASW METHDOS TO RESOLVE CONFLICTS Jury of Citiziens Indipendent Authority

12. New cycle-lane project The on-line Survey (A) (C) (B)

13. New cycle-lane project The on-line Survey Text mining Using text mining methods these answers were processed to consider people’s opinion linked to their socio-economic characteristics

14. The on-line survey New cycle-lane project

15. New cycle-lane project Example of choice matrix On diagonal cells there are satisfied people using the preferred transport mean The survey on-line and the web-gis

16. The analysis of the perceived urban spaces To investigate the perceived covering bycicle distance we asked about some possible well-known origin-destination travels New cycle-lane project

17. STUDIO SULLA MOBILITA’ KDD – Knoledge Discovery in Database KDD is a process constituted of many steps during which the data analyst, starting from raw, inconsistent and noise data can achieve interesting and actionable knowledge New cycle-lane project

18. Data Mining IF (Socio- economic, spatio-temporal attributes values) THEN (Indicators value of urban mobility choices) Decision trees may be very complex as they can have a huge number of internal nodes: nevertheless they are very understandable as they can easily be converted (IF-THEN) classification rules New cycle-lane project

19. Data Mining The input variables should be characterized by a maximum of 10/15 categories In our case it is necessary to aggregate the variable “activity start time” in the following ranges New cycle-lane project (1230-180) 20:30-03:00 (DC): Dinner/after dinner (1140-1230) 19-20:30 (PC): Before dinner (1020-1140) 17-19 (TP): Late afternoon (900-1020) 15-17 (PP): Early afternoon (810-900) 13:30-15 (DE): Lunch/immediately after lunch (750-810) 12:30-13:30 (PE): Before lunch (600-750) 10-12:30 (TM): Late morning (180-600) 3-10 (PM): Start morning

20. Orthogram about the variables “transport mean” and “activity type” The orthogram shows higher bike use density for the activities “eat” and “ bring things”, and a high value for bike use also to perform housewife activities and leisure activities. New cycle-lane project

21. Orthogram concerning “transport mean” and “time begin activity” variables Bike use (in cyan color) is more concentrated in the period immediately following lunch, late afternoon and before dinner. New cycle-lane project (1230-180) 20:30-03:00 (DC): Dinner/after dinner (1140-1230) 19-20:30 (PC): Before dinner (1020-1140) 17-19 (TP): Late afternoon (900-1020) 15-17 (PP): Early afternoon (810-900) 13:30-15 (DE): Lunch/immediately after lunch (750-810) 12:30-13:30 (PE): Before lunch (600-750) 10-12:30 (TM): Late morning (180-600) 3-10 (PM): Start morning

22. Extracted decision tree related to the target attribute “transport mean” Between all the 110 extracted rules, the considered ones for this research concern bike use and contain an high “support” value New cycle-lane project

23. Main rules extracted New cycle-lane project 94 Transport mean = bike Trip companion = 0 AND Activity = To Eat AND time begin activity = Time Lunch 55 Transport mean = bike Trip companion = 0 AND Activity = Daily Shopping AND Planned = Just in Time AND Time <= 45 min 70 Transport mean = bike Trip companion = 0 AND Activity = Bring Things AND Income = Low Support THEN IF Support THEN IF 54 Transport mean = bike Trip companion >= 3 AND Activity = To Study 64 Transport mean = bike (Transport mean = by walk) Trip companion = 0 AND Activity = Service AND Income = Low (High) 50 Transport mean = bike Trip companion = 0 AND Activity = No Daily Shopping AND Number Childrens = 0 53 Transport mean = bike (Transport mean = motorbicycles) Trip companion = 0 AND Activity = Leisure AND Number cars owned = 0 AND Sex = F (M)

24. THANKS FOR YOUR ATTENTION for further contacts: Massimiliano Petri Marco Rotonda m.petri@ing.unipi.it [email_address]