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Estimation of Optimal Passenger Car Equivalents of TCS Vehicle Types for Expressway Travel Demand Models Using a Genetic Algorithm

고속도로 교통수요모형 구축을 위한 유전자 알고리즘 기반 TCS 차종별 최적 승용차환산계수 산정

  • 김경현 (아주대학교 건설교통공학과) ;
  • 윤정은 (한국건설기술연구원 도로연구소) ;
  • 박재범 (한국도로공사 도로교통연구원) ;
  • 남승태 (한국도로공사 건설처) ;
  • 류종득 (한국도로공사 보은지사) ;
  • 윤일수 (아주대학교 교통시스템공학과)
  • Received : 2015.01.02
  • Accepted : 2015.05.20
  • Published : 2015.06.15

Abstract

PURPOSES : The Toll Collection System (TCS) operated by the Korea Expressway Corporation provides accurate traffic counts between tollgates within the expressway network under the closed-type toll collection system. However, although origin-destination (OD) matrices for a travel demand model can be constructed using these traffic counts, these matrices cannot be directly applied because it is technically difficult to determine appropriate passenger car equivalent (PCE) values for the vehicle types used in TCS. Therefore, this study was initiated to systematically determine the appropriate PCE values of TCS vehicle types for the travel demand model. METHODS : To search for the appropriate PCE values of TCS vehicle types, a traffic demand model based on TCS-based OD matrices and the expressway network was developed. Using the traffic demand model and a genetic algorithm, the appropriate PCE values were optimized through an approach that minimizes errors between actual link counts and estimated link volumes. RESULTS : As a result of the optimization, the optimal PCE values of TCS vehicle types 1 and 5 were determined to be 1 and 3.7, respectively. Those of TCS vehicle types 2 through 4 are found in the manual for the preliminary feasibility study. CONCLUSIONS : Based on the given vehicle delay functions and network properties (i.e., speeds and capacities), the travel demand model with the optimized PCE values produced a MAPE value of 37.7%, RMSE value of 17124.14, and correlation coefficient of 0.9506. Conclusively, the optimized PCE values were revealed to produce estimates of expressway link volumes sufficiently close to actual link counts.

Keywords

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