Dockless bike-sharing has experienced explosive growth, establishing itself as an integral component of urban public transport systems. Challenges such as parking violations have spurred operators and users to pursue standardized management. While electronic parking spots are employed to promote standard parking, suboptimal parking layouts can lead to illegal parking. Inadequate post-violation penalties fail to achieve standard parking, while excessive punishment diminishes user engagement. This study combines parking spot density and penalties to incentivize standard parking, and Beijing, China, was selected as the research object. Using an SP questionnaire survey, a binary logistic model analyzes bike-sharing users’ standard parking behavior and willingness to adhere to different rules. Findings reveal that optimal walking distances range from 300 to 450 m for service levels and exceed 400 m for service efficiency. Influential factors include gender, age, occupation, usage behavior, and travel preferences. Users with high-frequency, low-convenience expectations, low travel costs, and flexible travel exhibit strong adherence. Additionally, user acceptance of the maximum distances without penalties follows an exponential distribution, with 80% accepting 400 m and 40% accepting 800 m. Enforcement has a visible effect within 300 m, but diminishes with longer distances. Excessive penalties result in significant user loss.

It is important to understand the mechanism of travel location choices, since the dynamic individual behavior would lead to the evolution of the network. The existing location choice researches usually based on the static and perfect information, and analyzed each step of the travel independently. This research considers the interaction between individuals on a series of trips in the travel chain by incorporating the spatial-temporal bounded rationality estimation. The Indifference Zone and information sharing in the travel process are defined to explore the spatial and temporal sides. A within-day location choice model is developed based on the spatial benefit and temporal cost. The proposed spatial-temporal bounded rationality LSTM model is verified in five cities networks in China, and it shows the 8.65% and 20.30% improvement, respectively, compared to the spatial bounded rationality LSTM models and perfect rationality LSTM models. In addition, the improvement becomes more pronounced when more alternative locations (the largest city improve 31.65%), more serious network congestion (improve 27.45%), more obvious location composition path (improve 13.85%), and more stable within-day travel weight (improve 22.88%). The proposed dynamic decision model with bounded rationality would provide insights for travel chain prediction in the complex urban network.

Global climate change poses a critical challenge to sustainable urban development. The construction of low-carbon transportation systems is therefore a core strategy for enhancing the sustainability of mega-city road networks. Combining the characteristics of urban road traffic networks, this paper establishes a method for vehicle trip segmentation and carbon emission estimation based on GPS trajectory data (5699 vehicles, Beijing, September 2019) and the COPERT emission model, analyzing the spatiotemporal distribution characteristics of vehicle emissions. By incorporating the Life Cycle Assessment (LCA) emissions of electric vehicles, this study proposes carbon reduction strategies based on stochastic selection and ranking-based optimization from two dimensions: road-segment and vehicle electrification. Simulation methods are employed to evaluate the effectiveness of different strategies, as well as road network carbon emissions, under four vehicle electrification structures: Pyramid, Inverted Pyramid, Olive, and Dumbbell. Results indicate that carbon emission intensity rises significantly due to traffic congestion during peak hours. Under the LCA framework, Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) show significantly lower emissions than traditional Internal Combustion Engine Vehicles (ICEVs). Under the specified scenario assumptions, the ranking-based optimization scheme is estimated to yield carbon reductions approximately 2 times (segment control) and 3 times (electrification) those of the stochastic selection scheme, respectively. The study concludes that integrating EV promotion policies with precise carbon reduction control strategies can effectively mitigate urban road network carbon emissions.