A stochastic behaviour model of a personal mobility under heterogeneous low-carbon traffic flow

Abstract

Abstract The study proposes a mathematical framework to explain the stochastic behavioural patterns of personal mobility (PM) devices under low-carbon heterogeneous traffic conditions in shared lanes. We create a set of anticipation factors in a stochastic PM behaviour model to tackle sensitivities to both space headway and relative speed against intra- and inter-modes. The proposed behaviour model involves a deterministic and a stochastic force. In the deterministic force, the anticipation factors are used in an optimal velocity model and a full velocity difference model. In the stochastic force, the Langevin equation is used to capture PMs’ stochastic characteristics against movements of other PMs, pedestrians, and bicycles, and the effect of lateral interactions. We carried out real-world circular experiments of mixed sustainable modes to verify the performance of the proposed models. Five models’ performances are compared under four different traffic conditions, including bike-mixed, pedestrian-mixed, low-speed, and high-speed conditions. We confirmed that newly created anticipation factors play a significant role in all models under all conditions to partially influence the following PM devices’ behaviour from the leading two different sustainable modes. The validation results illustrate the excellence of the proposed method. Consequently, behavioural uncertainty is well captured by the stochastic PM devices following models under all traffic conditions, although it requires more parameters than the deterministic PM behavioural models. The proposed method paves the way for the stochastic CF model’s applicability to describe PM devices’ behavioural dynamics under mixed traffic conditions using anticipation factors. Besides, it lays the foundation stone of PM devices’ dynamics in a shared lane to construct effective regulations and safety standards.