The movement of past peoples in the landscape has been studied extensively through the use of least cost path (LCP) analysis. Although methodological issues of applying LCP analysis in archaeology have frequently been discussed, the effect of DEM error on LCP results has not been fully assessed. Due to this, the reliability of the LCP result is undermined, jeopardising how well the method can confidently be used to model past movement. To strengthen the reliability of LCP results, this research proposes the use of Monte Carlo simulation as a method for incorporating and propagating the effects of error on LCP results. Focusing on vertical error, random error fields are calculated and incorporated into the documented and reproducible LCP modelling process using the R package leastcostpath. By graphically communicating the impact of vertical error using probabilistic LCPs, uncertainty in the results can be taken into account when interpreting LCPs. The method is applied to a Roman road case study, finding that the incorporation of vertical error results in the identification of multiple ‘least cost’ routes within the landscape. Furthermore, the deviation between the roman road and the probabilistic LCP suggests that the location of the roman road was influenced by additional factors other than minimising energy expenditure. This research finds that the probabilistic LCP derived using Monte Carlo simulation is a viable method for the graphical communication of the uncertainty caused by error within the input data used within the LCP modelling process. Therefore, it is recommended that probabilistic LCPs become the default approach when modelling movement using input data that contains errors.

通过使用最小成本路径（LCP）分析，已经广泛研究了过去人们在景观中的运动。尽管经常讨论在考古学中应用LCP分析的方法论问题，但尚未完全评估DEM误差对LCP结果的影响。因此，LCP结果的可靠性受到损害，从而危及该方法可以可靠地用于建模过去运动的程度。为了增强LCP结果的可靠性，本研究提出使用蒙特卡洛模拟作为合并和传播误差对LCP结果的影响的方法。着重于垂直误差，使用R包最小成本路径来计算随机误差字段，并将其合并到已记录且可重现的LCP建模过程中。通过使用概率性LCP以图形方式传达垂直误差的影响，在解释LCP时可以考虑结果的不确定性。将该方法应用于罗马道路案例研究，发现垂直误差的引入导致在景观内识别出多个“成本最低”的路线。此外，罗马道路与概率性LCP之间的偏差表明，罗马道路的位置除了减少能源消耗以外，还受到其他因素的影响。这项研究发现，使用蒙特卡洛模拟法得出的概率LCP是一种可行的方法，可用于在LCP建模过程中使用输入数据中的误差引起的不确定性的图形通信。所以，