This study examines how Free-Floating Bike-Sharing (FFBS) affects the accessibility equity of public transit sytems by serving as a first-mile feeder. To evaluate accessibility improvements for various opportunities within a 30-min travel time, we construct a complete travel chain approach based on multi-source, real-world data from Nanjing, China. The results indicate that FFBS significantly enhances accessibility, particularly for job opportunities and green spaces, with improvements of up to 180.02% and 155.82%, respectively. This integration also enhances the accessibility equity of public transit systems, particularly in green spaces, with a Gini coefficient improvement of 0.0336. Additionally, we find that areas with low housing prices exhibit greater accessibility inequality, while those with moderate housing prices benefit more from FFBS integration. These findings can potentially support transport planners in optimizing and managing FFBS and public transit systems to facilitate sustainable and inclusive transportation networks.

Integrated transportation is one of the most important methods to encourage the modal shift from car to public transportation (PT). However, as most cities have an existing multimodal network, it is difficult to expand the current networks by building more PT routes. Thus, integrating different modes through the optimization of hubs is a cost-efficient way to promote sustainable mobility. This paper develops a bilevel multimodal network design problem based on the collaborative optimization of urban transportation hubs. The upper-level problem is formulated as a mixed-integer nonlinear program to achieve a modal shift from congested subnetworks to underutilized subnetworks to realize a balanced use of the entire network. The decision variables are classified into location-based (hub locations) and route-based (route layouts and frequency setting) ones. The lower-level problem is a generalized modal split/traffic assignment problem (GMS/TAP), which captures the mode choices of all modes in the path set. The GMS/TAP is formulated as a nonlinear optimization problem (NLP) and is solved using a hybrid method of the successive average (MSA) algorithm. A hybrid genetic search with advanced diversity control (HGSADC) is developed to solve the bilevel model, where the exploration of the search space is expanded using the biased fitness function and diversification mechanism. The solution properties of the hybrid MSA and HGSADC are demonstrated in two modified nine-node networks. The model performance is illustrated in a real-size network in Jianye district, Nanjing. 9.2% decrease of travel time, 25.7% increase of service level, and a significant modal shift from car to PT are obtained.

The high-speed railway (HSR) has profoundly influenced individuals’ lifestyles and travel behaviors. The development of HSR stations and their surrounding areas plays a critical role in urban growth, enhancing both transport efficiency and urban functionality. This study investigates the development of HSR station areas, with a particular focus on Shanghai Hongqiao station, emphasizing the enhancement of street vitality as essential for integrated urban development. Street vitality in station areas is closely associated with individuals’ activities and travel behaviors, influenced by walking accessibility and the built environment. Understanding these factors is crucial for improving the efficiency and attractiveness of HSR station areas. Although extensive research has examined the separate impacts of the built environment and walking accessibility on street vitality, a significant gap remains in comprehending their synergistic effects. This study employs GPS and point-of-interest (POI) data to analyze the stay time of HSR passengers in station areas. Utilizing machine learning algorithms and geographic information system (GIS) tools, this research models the impact of walking accessibility and the built environment on passengers’ stay time. The results indicate that passengers are more inclined to remain within areas accessible by a 7 min walk from the station. Furthermore, the synergistic effects of walking accessibility and the built environment can inform the spatial planning of various functions. These findings provide valuable insights for urban planners and policymakers aiming to enhance the development and efficiency of HSR station areas.

Developing public transportation is an effective way to alleviate traffic congestion and reduce fossil fuel consumption. However, the result of ineffective public transport planning can be that some bus lines have long overlapping intervals, and share many common stops between them, which causes a significant waste of resources. The current bus scheduling method does not consider the imbalance of passenger flow between different stops or combine the overlapping interval with transit mixed scheduling, including all-stop and stop-skipping services. Therefore, based on the analysis of the characteristics of overlapping interval, this paper proposes a combined bus scheduling method, which includes both all-stop and stop-skipping bus services. The method is a deterministic method applied in the scheduling stage of transit planning. The method optimizes scheduling strategies by minimizing total passenger travel time, including the waiting time and the in-vehicle travel time, and the line frequency of each bus service is taken as the decision variable. Then a genetic algorithm is designed to find the optimal solution. The model is verified by two bus lines in Changchun city. The experimental results showed that the proposed model could reduce total passenger travel time by 7.71% compared with the existing scheduling method. The combined bus scheduling method can supplement the research on bus scheduling of overlapping routes and provide suggestions for operators.