Promoting the use of public transportation is an important approach to develop sustainable mobility. However, lots of potential users of public transportation chose taxi, a semi-private mode for convenience. In this study, we first define this potential urban rail transit demand based on its spatiotemporal features. Then a novel data mining method is proposed to ascertain the potential urban rail transit demand from taxi trajectory data through considering spatial and temporal constraints simultaneously. Two features of the potential demand, i.e., the zero rates and volatility, are obtained by the combination of statistical and feature extraction (local neighbor descriptive pattern, LNDP) techniques. They are used to classify the urban rail transit stations into different categories which need different improvement measures to promote the attraction to the potential users. The effectiveness of the proposed method is tested using the GPS trajectory data of Shanghai collected from over 10,000 taxis in 12 consecutive days. We find that most urban rail transit stations have the potential to absorb the regular part of taxi ridership. Moreover, obvious imbalances exist between access and egress potential travel demands at these stations. The results show that metro stations can be classified into six groups according to the time-varying laws of potential travel demand, four of which need urgent measures. These findings provide useful insights for developing more effective and targeted strategies to encourage travelers to shift to public transportation. The estimated method of potential demand is the prerequisite for further optimization models. INDEX TERMS Public transportation, taxi GPS trajectory data, travel spatiotemporal pattern, urban rail transit station.

License plate restriction (LPR) policy presents the most straightforward way to reduce road traffic and emissions worldwide. However, in practice, it has aroused great controversy. This policy broke the original structure of the urban transportation mode, which needed some matching strategies to adapt to this change. Investigating this travel demand change is a challenging task because it is greatly influenced by features of the local built environment. Fourteen variables from four dimensions, location, land-use diversity, distance to transit, and street design, are used to depict the built environment; moreover, the severe collinearity underlies these feature variables. To solve the multicollinearity among the variables and high-dimensional problem, this study utilizes two different penalization-based regression models, the LASSO (least absolute shrinkage and selection operator) and Elastic Net regression algorithms, to achieve the variable selection and explore the impacts of the built environment on the change of travel demand triggered by the LPR policy. Travel demand changes are assessed by the relative variation in taxi ridership in each traffic analysis zone based on the taxi GPS data. Built environment variables are measured using the transportation network data and the Baidu Map Service points of interest (POI) data. The results show that regions with a higher level of public transportation service and a higher degree of the land mix have a stronger resilience to the vehicle restriction policy. Besides, the contribution rate of public transportation is stable as a whole, while the contribution rate of richness depends on specific types of land use. The conclusions in this study can provide in-depth insights into the influence of the LPR policy and underpin traffic complementary policies to ensure the effectiveness of LPR.

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