Using OD Estimation Techniques to Identify Parcel Freight Determinants and Improve the Parcel Delivery Services in Korea

Transcription

1 Using OD Estimation Techniques to Identify Parcel Freight Determinants and Improve the Parcel Delivery Services in Korea 2015 I-NUF Oct. 22, 2015 Sangbeom SEO, Jee-Sun LEE MetroFreight Center of Excellence Dept. of Logistics Research, The Korea Transport Institute (KOTI)

2 Contents 1. Introduction 2. Method : O/D estimation of parcels in Korea 3. Parcel Freight Determinants Identification 4. Summary and Future Directions 2

3 1. Introduction

4 1. Introduction Growth of Parcel Delivery Services in Korea Rapid growth in parcel delivery services with prosperity of e-commerce since 2000 Annual average in 2014, totally 1.62 billion boxes, 31 a person, 79 per household (source: KILA, 2014)

5 1. Introduction Motivations Developed to be an essential service for daily life in the last 15 years Inappropriate infrastructures and ineffective institution by public sector cannot still keep up with the development of services by private companies Issues rising : customer dissatisfaction increases, social costs as congestion Appropriate public policy and effective management strategies of region-specific in order to meet the parcel delivery services needs could be developed by understanding spatial pattern of parcel freight flows among regions Identifying determinants that affect parcels production and attraction by region

6 1. Introduction Study objectives Generating O/D matrix of parcels shipped between regions in Korea for 2014 estimates based on spatial pattern of parcel movement between 251 zones Exploring the spatial pattern of parcel freight movement Identifying the parcel freight determinants correlation analysis between parcel freight and socioeconomic attributes using selected socioeconomic factors based on OECD social Indicators and U.S. NCFRP factors on freight demand and traffic Literature review West, Rubin, and Villa (2011) identified and evaluated the freight demand factors in their final report of NCFPR project M.D. Anderson et al. (2013) explored the socioeconomic factors that determines freight in a medium sized community based on OD estimation techniques

7 2. Method : O/D estimation of parcels in Korea

8 2. Method : O/D estimation of parcels in Korea Procedure Classification Input data outcome 1 st step : Seasonality analysis 2 nd step : O/D estimation for 2 companies 3 rd step : Spatial pattern analysis 4 th step : O/D estimation -Monthly total freight volume from Jan 2009 to Jun 2015 (source: KILA) -Seasonality factor for each month -A specific month s freight flow O/D for two companies - Annual freight O/D for two companies - Spatial ratio matrix of freight flows -Total annual freight volume for 2014 (source: KILA) Seasonality factor for each month Two companies annual freight flow O/D Spatial ratio matrix of freight flows Annual freight flow O/D for the total volume

9 2. Method : O/D estimation of parcels in Korea Step 1: Seasonality analysis S 2012,1 = P 2012,1 / μ y=2012 S y,m : Seasonality Factor of Year y, Month m P y,m : Real Volume Data of Year y, Month m μ y=y : Average Volume of Year y, mil. Parcel

10 2. Method : O/D estimation of parcels in Korea Step 2: Estimation of freight flow O/D ( ) for two companies : O/D matrix of freight flow delivered by Company A during June in 2014 : O/D matrix of freight flow delivered by Company A during the year of 2014

11 2. Method : O/D estimation of parcels in Korea Step 3: Spatial pattern analysis of total freight flows Calculating spatial ratio ( ) of freight flow for individual O/D pair i and j using each cell s value ( ) of estimated O/D matrix in step 2 ( ) Constructing spatial ratio matix = ( )

12 2. Method : O/D estimation of parcels in Korea Step 4: O/D estimation for the whole country Estimating 2014 O/D matrix ( ) multiplying total freight volume of the whole county by spatial ratio ( )

13 2. Method : O/D estimation of parcels in Korea Microscopic approach obviously tells us more

14 3. Parcel Freight Determinants Identification

15 3. Parcel Freight Determinants Identification Indicators socioeconomic factors 17 variables as freight determinants selected based on literature review OECD indicators General: total pop., pop. growth rate, % aged, GDP, etc. Economy: emp., income, etc. Equity: GINI index, relative poverty rate, etc. M.D. Anderson et al. (2013) # total households, # lowincome households, # students, # emp. In service & sales, etc. NCFRP (2011) pop., pop density, # household, emp., freight volume, # establishments, etc. Demographic indicators pop density, # households, # single member households, index of aging, female pop. Income indicators # luxurious houses, fiscal self-reliance index, house leasing price * closure issues for income data because of use restrictions for inequity indices Industry indicators # mfg establishments, mfg emp. size, # sales establishments, sales emp. size Housing (land use) indicators # detached houses, # multi-household houses, # apartments, # townhouses, # multiplex houses

16 3. Parcel Freight Determinants Identification Correlation analysis results Inflows Positive correlations with : # single-member households, female pop., all multi-unit dwellings, # sales establishments, wealth Negative correlation with : aging index Outflows Positive correlations with : numbers of mfg. and sales, fiscal selfreliance index Negative correlations with : again index, # detached houses, house leasing price index Demographic Housing (Land Use) Industry Income Variables IN_flow OUT_low Population Density No. of Households 0.224** No. of 1-person Households 0.226** Index of Aging ** ** Female Population 0.219** No of Detached Houses ** No of Multi-Family Houses 0.219** No of Apartments 0.328** No of Townhouses 0.302** No of Multiplex Houses 0.249** No of Manufacturers ** No of Manufacturing Employees No of Sales Establishments 0.286** 0.274** No of Sales Employees 0.243** 0.263** No of Luxurious Houses 0.146* Fiscal Self-Reliance Index 0.252** 0.340** House Leasing Price Index * ** significant at the significance level of 0.01, * significant at the level of 0.05

17 3. Parcel Freight Determinants Identification Spatial pattern of parcel freight flows Big differences between urban and rural area Urban areas occupy most scales of both in- and out-flows of parcel freight classification Inflow urban rural total urban 1,419.4 (87.44%) (6.49%) 1,524.8 (93.93%) Outflow rural 91.4 (5.63%) 7.1 (0.44%) 98.5 (6.07%) total 1,510.8 (93.07%) (6.93%) 1,623.2 (100.00%)

18 4. Summary and Future Directions

19 4. Summary and Future Directions Inflows Multi-unit dwellings, # single-member households, female population, industrial structure, income level would attract more parcels to each region Policy implementations : mandatory installation of parcel lockers within the multi-unit house complexes over 500 households (Aug, 2014), female-friendly parcel drop & pickup points development for safety (since 2013) New policy planning to guarantee parcel service equity for remote areas Outflows Obviously related to regional/urban industrial structure Parking regulations, loading/unloading zoning, and consolidation policy need developing Future Research Refine the parcel freight O/D matrix estimation Develop the method to forecast parcel freight volume by region through analyzing the trend of regional socioeconomic factors over time