EMME/2 Users Conference — October 2003 Data Collection and Model Development in Sarawak, East Malaysia Edwin Hull, TSi Consultants, Burnaby BC and Alastair Burns, Wilbur Smith Associates, Hong Kong.
Public Transport • Four private bus companies • No operating subsidies • Fares strictly controlled • Inadequate revenues to upgrade • Old inadequate buses
Public Transport • Licensed van service • Un-licensed (illegal) van service • Tambang (Water Taxis)
Public Transport • 30% decline over past five years • 11% of total trips • Low density development (sprawl) • Increasing traffic congestion
Kuching Public Transport Study • Long Term (2020) Master Plan • Medium Term (2010) Master Plan • Short Term Action Plan • Maintain & improve PT • Address overall transportation system
Kuching Public Transport Study • Develop EMME/2-based Model • Calibrate to 2002 Conditions • Apply to 2020 and 2010
Data Collection • Road network inventory • PT routes & service characteristics • Demographic & Land Use Data • Household Interview Surveys • Roadside interview surveys • Traffic & passenger screenline counts
HOUSEHOLD INTERVIEW SURVEY • 1250 Interview Administered Surveys Randomly Selected from Study Area Population • Household Socio-Economic & Travel Characteristics • - Purpose of Travel • - Frequency & Timing of Trips • - Mode of Transport • - Origin-Destination Patterns
OTHER SURVEYS • Other Vehicle/Passenger Surveys - Tambang Passenger Counts - Bus Corridor Survey (4 locations) - Special Generators [Hospital, Airport & UNIMAS) -Taxi/Minibus/Bus Terminal Surveys • Attitude Surveys - Private Van Talking Point Informal Interview - Bus/Minibus/Tambang Passenger Attitude Surveys - Hotel Guest Transport Survey
Data Problems • Bus service discrepancies • Low RSI sample • Illogical HIS data • Significant HIS under-reporting • Bus passenger volume reconciliation
Data Synthesis Extensive Use of Demand Adjustment and other Techniques to Match Assigned Matrices to Screenline Volumes
4 3 1 2 Screenline & Cordon Locations
Data Synthesis • Aggregate HIS data to 12 districts • Expand by household control totals and generate 24-hour matrices. • Develop hourly matrices by mode. • Compare with observed counts and develop adjustment factors • Expand adjusted matrices to match observed volumes. • Develop provisional trip generation equations based on “expanded” data.
Data Synthesis • Generate provisional trip ends by zone. • Disaggregate expanded trip matrices by provisional trip ends. • Assign disaggregated matrices. • “Demand adjust” to fit observed screenline counts. • Develop Demand Adjustment Factors by Time Period • Develop “Blended” Daily Adjustment Factors by Trip Purpose
Model Observed % error Bus Screenline North 1699 1803 -5.8% South 39074 34843 12.1% River 15516 14584 6.4% Total 56289 51230 9.9% Tambang (Water taxi) Screenline River 8004 7722 3.7% Calibration Results Table 5 : Modeled & Observed Bus & Tambang Passengers by Day
Model Application • Road and junction improvement concepts • Public transport project concepts • Land use/demographic scenarios • Policy scenarios
Study Findings • Road building alone cannot meet objectives • Public transport improvements cannot meet objectives • Integrated Multi-Modal Plan Required • Supported by PT-friendly land use and some TDM policies
Conclusions • Reliable data is essential to the development of a transport model. • Close supervision of all surveys essential with inexperienced local staff • Demand adjustment is a valuable tool where there are errors in data. • Caution required in the interpretation and application of model.