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This research reaffirms a sustainable transport investment should go forward in a way that enforces consistent economic growth while maintaining environmental benefit. Society's economic input-output relations reinvest the time saved by the transport system, of which benefit appeared as the income level's rise. This research empirically proves that the recursive profit is possible when the infrastructure aligns accessibility enhancement with industries' productivities. Specifically, we found that the interactions between industries and accessibilities make synergy when the industries maintain appropriate productivity. Under the same context, the measure for the socially vulnerable should start at capacity building even given transport, based on which we can build economically equitable accessibility. This research takes environmental sustainability granted as long as the incumbent Korean infrastructure investment principle exists without empirical analysis. A comparative analysis using structural equation models proved the existence of the synergy due to the accessibility-industry interactions. Besides, we captured the quantities of the synergy through groups of partial cross-sectional correlations. Authors conclude that government authority needs to pursue the best synergy between transport plan and economic development strategy, and prepare a capacity building for the socially vulnerable furthermore. [ABSTRACT FROM AUTHOR]

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]

Cycling is gaining popularity both as a mode of travel in urban communities and as an alternative mode to private motorized vehicles due to its wide range of benefits (health, environmental, and economical). However, this change in modal share is not reflected in current transportation planning and travel demand forecasting modeling processes. The existing practices to model bicycle trips in a network are not sophisticated enough to describe the full cyclist experience in route decision-making. This is evident in the existing practices' methodology: the all-or-nothing assignment uses single attributes such as distance, safety, or a composite measure of safety multiplied by distance. The purpose of this article is to develop a multi-class and multi-criteria bicycle traffic assignment model that not only accounts for multiple user classes by acknowledging that there are different types of cyclists with varying levels of biking experience, but also for relevant factors that may affect each user classes behavior in route choice decisions. The multi-class, multi-criteria bicycle traffic assignment model is developed in a two-stage process. The first stage examines key criteria to generate the set of non-dominated (or efficient) routes for each user class, and the second stage determines the flow allocation to efficient routes by user class. Numerical experiments are then conducted to demonstrate the two-stage approach for the multi-class, multi-criteria bicycle traffic assignment model. [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]