Calibration of VISSIM Parameters for Modeling Heterogeneous Traffic Conditions at Intersections in Kathmandu

Aashish Manandhar — 2026-01-02

Kathmandu’s intersections, characterized by heterogeneous traffic conditions, present unique challenges in traffic modeling. Dominated by motorcycles, these intersections feature diverse vehicle types, exhibit side-by-side vehicle stacking, variable lane widths, and the absence of lane markings and road discipline. In Nepal, microsimulation models are typically developed using trial-and-error methods and are not properly calibrated to such conditions. This study proposes an automated calibration approach for microscopic traffic simulation models, providing calibrated parameter values tailored to local traffic conditions. Three intersections were modeled using VISSIM, a microscopic simulation tool. Sensitivity analysis was conducted using Latin Hypercube Sampling (LHS) and two-level ANOVA testing to identify nine sensitive calibration parameters. Optimization using a bi-level genetic algorithm minimized the error between the simulated and field traffic flow and queue-length data. This resulted in the following sensitive calibration parameters with the recommended range of values—minimum look ahead distance (10−20), look back distance [minimum (15−18.71) and maximum (107.94−150)], average standstill distance (0.3−1.5), safety distance part [additive (0.1−0.5) and multiplicative (0−1)], minimum clearance (front/rear) (0.49−0.73), and minimum lateral distance [standing (0.2−0.41) and driving (0.6−0.9)]. When validated against the videographic survey data, the outputs appear similar to the findings from Indian studies. However, the tolerance levels of the outputs suggest that we need our values rather than Indian values. This methodology and the outputs are expected to significantly reduce the time and effort required for calibrating VISSIM models in similar traffic environments.

Modelling Tanker Transportation Demand in Nigeria’s Crude Oil Market

Kenneth O. Okeke — 2026-02-03

Tanker shipping is a critical component of Nigeria’s crude oil export system and is largely driven by global economic conditions and shipping market dynamics. This study models tanker transportation demand in Nigeria’s crude oil market using annual time series data spanning 1990–2019. An auto-regressive distributed lag-unrestricted error correction model (ARDL-UECM) is employed to account for mixed orders of integration and to estimate both short-run dynamics and long-run elasticities. Tanker transportation demand is specified as a function of World GDP per capita, Brent and Bonny Light crude oil prices, global tanker fleet capacity, freight rates, piracy incidents, tanker accidents, ship turnaround time, and world population. The results confirm the existence of stable long-run equilibrium relationship among the variables. World GDP per capita emerges as the dominant determinant of tanker transportation demand, with a 1% increase in global GDP per capita leading to an estimated 2.55% increase in tanker transportation demand in the long run. In contrast, piracy incidents, tanker accidents, freight rates, and ship turnaround time exert negative but statistically weak effects, indicating that their effects operate through lagged and broader market mechanisms rather than direct short-run responses. Diagnostic tests, including the CUSUM and CUSUM of squares, confirm both parameter and variance stability of the estimated model. The study contributes to maritime economics literature by providing a unified ARDL-based tanker demand model that integrates global macroeconomic forces, shipping market dynamics, and Nigeria-specific operational risks, offering valuable insights for maritime policy, port efficiency planning, and tanker market forecasting.

International Journal of Traffic Management in Transportation Network (p: 3107-9504)

Calibration of VISSIM Parameters for Modeling Heterogeneous Traffic Conditions at Intersections in Kathmandu

Modelling Tanker Transportation Demand in Nigeria’s Crude Oil Market