Stratified Assessment of Urban Low-Carbon Travel Potential
DOI:
https://doi.org/10.7307/ptt.v37i1.710Keywords:
urban low-carbon travel potential, non-motorised travel, public transport travel, connection, delay, transferAbstract
Non-motorised travel and public transportation travel are recognised as low-carbon travel modes, in contrast to car travel, which is considered a non-low-carbon option. Based on this, the paper proposes a stratified assessment method for the urban low-carbon travel potential. The proportion of the motorised travel population that could potentially shift to non-motorised travel within the entire travel population is defined as the urban Tier 1 low-carbon travel potential. Meanwhile, the proportion of the car travel population that could potentially shift to public transportation travel within the entire travel population is defined as the urban Tier 2 low-carbon travel potential. This method holistically presents the potential for improvement in urban traffic carbon emission control. This method considers distance as a primary negative factor affecting the residents’ willingness to engage in non-motorised travel compared to motorised travel. Additionally, it recognises connection, delay and transfer as the main negative factors influencing the residents’ willingness for public transportation travel over car travel. By comparing the actual travel distances of residents and the actual intensity of connection, delay and transfer in public transportation travel modes with the assumed maximum acceptable distances and intensity for residents, the method identifies the number of people who could potentially shift to corresponding levels of low-carbon travel in hypothetical scenarios. Based on this, the corresponding low-carbon travel potential values are calculated. The method then further analyses the trend of these values as the residents’ acceptable thresholds for non-motorised travel distances and acceptable intensity for public transportation travel connection, delay and transfer change. A relationship curve is fitted, which intriguingly exhibits a reverse “S” shape, allowing for the identification of the “rapid release zone” and “key points” on the curve. These insights are essential for effectively targeting interventions to increase the adoption of low-carbon travel modes. This paper takes the cities of Shanghai and Wuhan in China as examples, conducting a stratified assessment of the low-carbon travel potential for both cities based on 19,732 daily travel origin– destination (OD) survey samples from residents. Additionally, the low-carbon travel potential of the two cities is visualised by district, enabling an analysis of the characteristics of low-carbon travel potential in each city and a comparison of the differences in low-carbon travel potential between them.
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