Using Data and Models for Informed Decision-Making in Urban Freight Planning

Transcription

1 Using Data and Models for Informed Decision-Making in Urban Freight Planning

2 VREF conference Break out session - USING DATA AND MODELS FOR AN INFORMED URBAN FREIGHT PLANNING AND DECISION MAKING Mathieu Gardrat, LAET

3 Mathieu Gardrat, VREF Conference Gothenburg, October 2016 Freight data collection, modelling and public policies What uses? Diagnosis? Urban planning projects? (medium term) Strategy (transport and urbanism master plans)? (long term) What means? For what purposes in urban policies? Pollution, Safety, Noise, Competitiveness, Congestion

4 Mathieu Gardrat, VREF Conference Gothenburg, October 2016 Data, modelling and decision-making process Diagnosis = Identifying the stakes Surveys (most expensive) Combining existing data Modelling Prioritise: Strong policy Identifying the easiest / most efficient measures Simulation = identifying the relevant measures and their effects Modelling (forecasting) Ex-post evaluation 3

5 Mathieu Gardrat, VREF Conference Gothenburg, October 2016 General informations Phone contact UGM surveys (France) Establishment survey Log book (1 week) Pick-ups /deliveries Driver survey Driver questionnaire First visit Description of pick-ups and deliveries Goods Route Second visit Stops Carrier survey Trip sections (source : Toilier et al. 2016) 4

6 Mathieu Gardrat, VREF Conference Gothenburg, October 2016 Combining data sources (France) Surveys and transport data capture: Freight transport surveys (mandatory) Light commercial vehicles surveys (mandatory) UGM surveys Shipper surveys Consumer surveys (end-consumer trips, e- commerce) Data from local authorities where freight transport can be identified Traffic counts Accidents Infractions (parking) Roadside/Cordons surveys Regulation Data giving information on freight transport indirectly (modelling) Census Establishments databases Infrastructures Real estate Vehicle fleets (licence plates) Data from the private sector, big data 5

7 Mathieu Gardrat, VREF Conference Gothenburg, October 2016 Combining data sources (Lyon) (source : Urban freight observatory for the greater Lyon, 2016) 6

8 Mathieu Gardrat, VREF Conference Gothenburg, October 2016 Freight modelling 7

9 Mathieu Gardrat, VREF Conference Gothenburg, October

10 Mathieu Gardrat, VREF Conference Gothenburg, October 2016 (source : SIRENE 2012, Freturb) 9

11 Mathieu Gardrat, VREF Conference Gothenburg, October 2016 (source : open data Grand Lyon) 25% 20% 15% 10% 5% 0% Distribution of movements during the day (source : SIRENE 2012, Freturb) (source : SIRENE 2012, Freturb) 10

12 Mathieu Gardrat, VREF Conference Gothenburg, October 2016 (Lyon ; source : SIRENE 2012, Freturb) 11

13 Mathieu Gardrat, VREF Conference Gothenburg, October 2016 (Geneva ; usource: Freturb, VISUM, 2010) 12

14 Mathieu Gardrat, VREF Conference Gothenburg, October 2016 Freight transport simulation Prospective Freight transport in urban planning: Evolution of the urban structure (economy, infrastructures) Evolution of logistics organisations Urban structure Urban structure N+ N+1 Logistics organisations Logistics organisations N Urban structure Logistics organisations Diagnosis 13

15 1 18 OCTOBER 2016 Using data for informed decision-making in urban freight planning Ian Wainwright Head of Freight and Fleet Programmes VREF Conference on Urban Freight 2016

16 2 Why do we need data on freight? Deliveries and servicing impacts street performance Loading location not always matched to the delivery and servicing demand in an area Delivery and servicing vehicle activity is driven by convenience; drivers use kerbside, parking bays, bus stops and even pedestrian crossings Legal provision is not always available Has a knock on effect on traffic flow, congestion and air-quality, pedestrian permeability, safety and the overall environment of the street

17 3 Street surveys - a new methodology Scheme Feasibility 1 How to survey 2 How to manage 3 Without extra time Preliminary Design Consultation Detailed Design Implementation Post implementation

18 4 Data Collection Local data collection including land use is key

19 5 What the data shows Freight activity is approx. 7% of all kerbside activity Activity is mainly food, office and waste Land use is key (high street v high road) All streets are different (even the same type/category) Deliveries occur every day, both weekday and weekend activity varies Peak times differ, even without restrictions Physical access barriers affect delivery location Front access is preferred, even when rear facilities available Data in isolation doesn t show a broken street but comparing data can show if one area is better or worse It needs a LOCAL approach - one size doesn t fit all...

20 6 Using the data to identify the conflict... On site On street Shared

21 7 What next? Demystify freight for designers: it s goods and services What s the hook? Safety, air quality, congestion, place? Look for non-freight issues: sometimes it s parking, pedestrian access or improving the trading environment Methodology must be flexible Embed delivery and servicing activity in all future projects and schemes using project governance approach Involve all stakeholders: operators, business, regulators, politicians, city/local authorities

22 8 Contact

23 DATA DRIVEN LOGISTICS PUBLIC POLICY: BOGOTÁ CASE Juan P Castrellón Manager of the Public Private logistics office for Bogotá - Cundinamarca October 18th, 2016

24 Agenda 2 Logistics information: a coordination issue Bogotá s approach to Data Driven Logistics public policy Trends and opportunities

25 Logistics information: a coordination issue Urbanfreight deliveries information 1 What the sales people thinks it is 2 What the driver thinks it is 3 What the logistics service provider thinks it is 4 What the Logistics Platform manager think it is

26 Logistics information: a coordination issue Action of managing dependencies among entities and their joint efforts to achieve the established common goals (Wang, Zhou, & Wang, 2010).

27 Bogotá s approach to Data Driven Logistics public policy Institutional framework Bogotá Collaborative strategy between public and private agencies for the implementation of the Regional Logistics Plan" The office s mission is to encourage initiatives that facilitate urban-regional logistics activities with the objective of improving the competitiveness of Bogotá and Cundinamarca through the articulation of the public, private and academic sectors. Cundinamarca

28 Bogotá s approach to Data Driven Logistics public policy Logisticsinformationin Bogotá Especial projects Private sector information Freight Flows Economic development Infrastructure availability Territory planning

29 Bogotá s approach to Data Driven Logistics public policy Logisticsinformationin Bogotá

30 Bogotá s approach to Data Driven Logistics public policy Logisticsinformationin Bogotá Challenges Freight generation rates per logistics zones Detailed logistics information about city micro zones Analysis Space usage and interest from private sector to invest in particular projects and zones Challenges Analysis Decisions Decisions Territory plans and infrastructure focused on logistics Logistics performance index for municipalities

31 Bogotá s approach to Data Driven Logistics public policy Logisticsinformationin Bogotá Origin destination matrix Freightvehicle per corridor

32 Bogotá s approach to Data Driven Logistics public policy Logisticsinformationin Bogotá Parking zones availability Bay availability On Street availability Loading and unloading activities in the city

33 Bogotá s approach to Data Driven Logistics public policy Logisticsinformationin Bogotá Challenges Real time information Platforms for sharing information from private sector Challenges Decisions Analysis Decisions Analysis

34 Bogotá s approach to Data Driven Logistics public policy Logisticsinformationin Bogotá Off-hour deliveries in Bogotá

35 Bogotá s approach to Data Driven Logistics public policy Logisticsinformationin Bogotá Scorecard built directly from private sector information

36 Bogotá s approach to Data Driven Logistics public policy Logisticsinformationin Bogotá Challenges Open platforms for sharing information Analytics Challenges Decisions Decisions Programs leaded by public and private authorities Analysis Private companies performances Needs from the private sector Analysis

37 Trends and opportunities Enablers of coordination in logistics data sharing strategies The model of a collaborative process of local government as a policy maker requires to take into account the following facts (Witkowski, J. & Kiba-Janiak, M., 2014): Web 2.0 & social networking A collaborative process should include all stakeholders of city logistics and starts from the stakeholders needs analysis. Dialogue is a key principle that should build trust among all stakeholders. There must be considered a process of mutual understanding of the needs and expectitations as well as stakeholders involvement. Advanced analytics Open standards and ecosystems Open Access to public data Geospatial platforms Digitally controlled devices Internet of things All the stakeholders should be involved in developing strategic plans, using available tolos and methids. Ubiquitous connectivity Anytime / anyplace devices Collaboration platforms Cloud computing Source: (Katarzyna Nowicka, 2014)

38 Thank you! JUAN PABLO CASTRELLON Manager of the Public Private logistics office for Bogotá Cundinamarca

39 Applications of Data from Establishment Surveys 1

40 Data Problem? or Knowledge Problem? 2

41 The main challenge 3 Lack of knowledge, not lack of data The way to overcome the challenge Obviously, taking steps to enhance knowledge

42 Knowledge, Models, and Data 4 Data do not necessarily lead to Knowledge, D K Models cannot be developed without Knowledge Knowledge/Model inform Data collection: K+M D We need integrative developments: K+M+D Knowledge Models Data

43 Implications, treating the challenge as: 5 a data problem ( the Brute Force approach ): Bad idea, it is not feasible to collect all data we need: Too expensive Time consuming Will not provide the knowledge needed as an information inference problem: Cost effective: Key data are collected, models used to infer data gaps Requires research on freight modeling (a good thing) Model improvements would lead to better inference and better data collection procedures

44 Main Uses To gain insight into the commercial sector s perception of priorities, issues, etc. To quantify commercial establishment demand for freight and services To determine the behavioral response from the commercial sector to public sector initiatives (projects, programs, policies) 6

45 Pros/Cons of Establishment Surveys (and Models) Commercial Establishment Surveys: Provide a better connection between an expression of commercial activity and the attributes of the establishment Expression of commercial activity freight generation, freight trip generation, service trip generation, mode choice etc. Attributes of the establishment size, commodity type(s), line of business, industry sector, etc. Require less data than aggregate approaches, are more efficient using the data Lead to better models Could be aggregated to any level of geography Require a solid idea about the sectors that create demand...

46 Industry Sectors in Metro/Micropolitan Statistical Areas NAICS Freight-intensive Sectors (FIS) NAICS Non-freight-intensive Sectors (non-fis) 11 Agriculture, Forestry, Fishing, Hunting 51 Information 21 Mining, Quarrying, Oil / Gas 52 Finance and Insurance 22 Utilities 53 Real Estate and Rental and Leasing 23 Construction 54 Professional,Scientific,Tech. Services Manufacturing 55 Management of Companies / 42 Wholesale Trade 56 Administrative,Support,Waste Manag Retail Trade 61 Educational Services Transportation and Warehousing 62 Health Care and Social Assistance 72 Accommodation and Food Services 71 Arts, Entertainment, and Recreation 81 Other Services 92 Public Administration 45% of commercial establishments and 50% of employment 8 2.8% of commercial establishments and 3.6% of employment

47 Key Considerations The Industry Sector, Establishment Size, and Type of Commodity transported (somewhat determined by Industry Sector) heavily influence: Freight Generation Freight Trip Generation Service Trip Generation The behavioral attitude towards public sector policies Understanding behavior and generation patterns at the establishment level is essential Commercial Establishment Surveys are KEY 9

48 Case #1: Freight Generation, Freight Trip Generation, and Service Trip Generation

49 FG/FTG/STG analyses To estimate the amount of freight, freight-trips, and servicetrips Key for planning, analyses of parking demand, traffic impact analyses, and land use planning at a fine level of detail Generation of demand/cargo (FG): A manifestation of the production/consumption processes Implication: FG will increase with (economic) inputs Generation of traffic (FTG): Result of logistical decisions Implication: FTG may not increase with (economic) inputs Generation of Service Trips (STG): Determined by needs for outside support

50 Background Based on surveys: Establishment-level surveys CFS microdata Estimated FTG models Establishment-level Economic based Using publicly available data as independent variables Validated them Outperformed the alternatives Free-software available The second phase produced models to estimate freight generation, freight trip generation, and service trip generation (to be released later this year) This presentation is based on the 12 old models

51 Survey form (from NCFRP Report 19) 13

52 Freight Trip Generation (FTG) Software Tool to identify main locations where freight is an issue in terms of freight trips produced and attracted. It applies FTG models at ZIP code and 2 digit NAICS codes. Module 1: preprocesses County Business Pattern data from the census bureau and generates a database at a ZIP Code and 2 digit NAICS code level. Module 2: applies FTG models and produces estimations of freight trip attraction (FTA) and production (FTP). Module 3: allows modifying the coefficients used in the estimation of FTG models and selecting employment size Advanced features: 4 types of models available 14 Freight Trip Generation Estimator:

53 FTG at Metro/Micro-politan Areas 15 Thousands Total FTG Total of establishments Total employment FTG = * Population FTG = * Establishments FTG = * Employment 0 Population (Millions)

54 Freight Trip Generation by ZIP Code Long Beach Portland 16 Houston DC-Baltimore

55 Case #2: Freight Behavior Research to Support Freight Policy Making

56 Supply Chains Need to Change Behavior (Not Only the Carriers) The shippers The carriers The receivers The Economy 18

57 Successful freight policy-making requires: A clear understanding of: The role of the key agents: shippers, carriers, and receivers What they could decide on their own, and what could not What decisions they need to make for change to happen The policies and policy targets to induce the desired change Policies that require multi-agent coordination work much better if the policies target the agent with most power (which typically is not the carrier) Complex problems require complex solutions, there is no way to escape complexity In complex problems, obvious solutions are almost always wrong 19

58 Freight Behavior Research Conducted partial list Technology acceptance by freight carriers: Container Status Inquiry Systems Electronic Toll Collection systems Reactions to time-of-day tolls Reactions to time-distance tolls in intercity travel Policies to foster off-hour deliveries Carrier focused Receiver focused (Receiver) Inclination to consolidate deliveries to increase sustainability Freight/vehicle mode choice

59 Behavioral research conducted Behavioral surveys assessed the responses of carriers/receivers to policies to induce Assisted/Unassisted Off-Hour Deliveries Assisted OHD: 400 carriers and 400 receivers Unassisted OHD: 260 receivers Policies studied (Assisted OHD): Carriers: Customer request from receivers, request & toll savings. request & parking, request & paid permit Receivers: Financial incentive, Shipping discounts Policies studied (Unassisted OHD): Receivers: Financial incentive, Shipping discounts, Trusted Vendors, Business Support Services, Public Recognition The data were used to estimate behavioral choice models 21

60 Assisted OHD: Behavioral Research Findings Freight Carriers: All of them are HEAVILY influenced by the receivers decision on time of delivery Only the carriers in Petroleum/coal, Wood/lumber, Food, Textiles/clothing are sensitive to tolls Receivers of supplies: All receivers are moderately sensitive to tax deductions Receivers in these sectors are more sensitive than the rest: Wood, Alcohol, Paper, Medical Supplies, Food, Printed Materials, and Metal 22

61 Unassisted OHD: Behavioral Research Findings Key determinants in participation in Unassisted OHD: One-Time-Incentive (financial) Discounts from vendors (financial) Business support is worth Public recognition is worth Trusted vendor is worth Suggestion: Public sector Incentives and public recognition Carriers/vendors Shipping discounts Industry groups Create a Trusted vendor program 23

62 Freight Behavior Research Instrumental in the success of the OHD project Helped us find the: Right industry segment Right combination of incentives Right amount of incentives The prevailing wisdom was that OHD would not work, freight behavior research cleared the path for a transformative project 24

63 How could we improve these techniques?

64 Recommendations for Improvements The models are as good as the data Although progress has been made, improvements are needed Collect freight generation, freight trip generation, and service trip generation from different jurisdictions Use the pooled data to estimate models Foster use of freight behavior research to support policy making In complex systems like this one, professional intuitions though valuable are not always right Data driven policy making is key to facilitate implementation More experience is needed to develop a stronger knowledge base 26

65 Resources from TRB NCFRP Report 19: Freight Trip Generation and Land Use (2012) NCHRP Synthesis 410: Freight Transportation Surveys (2011) NCHRP Synthesis 385: Forecasting Metropolitan Commercial & Freight Travel (2008) 27

66 Additional Resources FMIP website: TMIP website: SHRP2 Freight Demand Modeling and Data Improvement (Project C20): ight_demand_modeling_and_data_improvement Freight Trip Generation Estimator: 28

67 Using Data and Models for Informed Decision-Making in Urban Freight Planning

68 Results from Mentimeter

69 Results from Mentimeter

70 Results from Mentimeter

71 Results from Mentimeter

72 Results from Mentimeter

73 Results from Mentimeter

74 Results from Mentimeter

75 Results from Mentimeter

76 Thanks for attending the break out session! Iván Sánchez-Díaz