COMPUTATIONAL MODELING AND SIMULATION TECHNIQUES FOR MANAGING RAIL–URBAN INTERFACE CONSTRAINTS IN METROPOLITAN TRANSPORTATION SYSTEMS

Abstract

Rail–urban interface constraints in metropolitan transportation systems reduce reliability and safety because dense stations, corridor conflict points, and community exposure can turn small disruptions into network-wide delays and crowding. This study examined whether computational modeling and simulation capability (CMSC) improves constraint-management effectiveness (CME) in an enterprise-scale metro rail case were cloud and enterprise analytics support scenario testing and decisions. Using a quantitative, cross-sectional, case-based design, a 5-point Likert survey was administered to N = 312 professionals from operations/control (26.3%), planning/timetabling (20.5%), station management (17.9%), engineering/maintenance (19.9%), and safety/risk (15.4%); 41.0% were direct model users and 37.8% indirect users. Key variables were rail–urban interface constraint severity (RICS), CMSC, decision integration (DI), and CME. Data screening showed mean missingness of 1.8% and Harman single-factor variance of 32.6%. Reliability was strong (α: RICS .88, CMSC .91, DI .87, CME .90). The most severe constraints were station crowding/circulation (M = 4.21, SD = 0.62) and peak dwell-time variability (M = 4.08, SD = 0.67). CMSC was moderate-high (M = 3.78, SD = 0.64), with scenario analysis strongest (M = 3.92) and validation weakest (M = 3.49). CMSC and DI correlated positively with CME (r = .62 and .58; p < .001), while RICS correlated negatively (r = −.41; p < .001). Regression was significant (R² = .51): CMSC (β = .38) and DI (β = .29) increased CME, and RICS reduced it (β = −.17); CMSC benefits were stronger at higher DI (ΔR² = .03). Implications emphasize station-area actions, stronger validation governance, and institutionalized use of simulation outputs in routine decisions.