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Distinguishing commuters from non-commuters is important for transportation planning and traffic demand management. A framework to identify commuters using public transit is proposed based on a spatial-temporal clustering algorithm. The framework extracts commute trips by mining spatial-temporal travel patterns depending on whether the travelers' trip chains are complete. The commuting features in terms of travel frequency, time regularity, and spatial regularity are utilized to compare the difference in travel behavior between commuters and non-commuters. The framework is applied to trip data collected from an incentive program implemented by the Metropolitan Transport Commission of Korea. The data records spatial and temporal information on travelers' boarding and alighting points, as well as actual origins and destinations. One trip record constitutes a complete trip chain for travelers who joined the incentive program. The results show that commuters exhibit a higher travel frequency and more regular spatial-temporal travel patterns than non-commuters. It is found that not all commuters travel during morning and evening peaks; some commuters leave work after evening peak. The proposed identification framework based on the spatial-temporal clustering approach is capable of exploiting the inherent spatial-temporal travel patterns of travelers to achieve a reliable identification of commuters. [ABSTRACT FROM AUTHOR]

• The Langevin force explains stochastic volatility in the behaviour model. • Anticipation factors define impacts of spacing and speed to adjacent modes. • Stochastic models cover fine fluctuations in speed profiles under varied conditions. 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. [ABSTRACT FROM AUTHOR]