As the outcomes of rapid urbanization, the spatial separation of homes and workplaces extends the commuting distance and complicates the commuting demand of residents. To promote urban livability and sustainability, it becomes crucially important to understand the commuting patterns by decomposing and simplifying the diverse commuting demand. In this paper, a methodology framework is proposed to describe the spatial structure of commuting demand in a city using mobile phone data. Four steps are mainly included in the proposed methodology: the preprocessing of mobile phone data, the labeling of individuals and their activity points, the construction of the jobs-housing relationship network, and the network decomposition based on the community detection algorithm. To demonstrate the practical use of the proposed methodologies, a case study is carried out in Shanghai to explore the commuting patterns of Shanghai residents. The result indicates the regions with dense jobs-housing connections and cross-regional commuting demand. The result also finds that the administrative boundaries show a significant effect on the residential commuting behavior and the metro lines on the cross-regional commuting behavior. The results generated by the methodology proposed can be referenced by policymakers to support urban transportation planning and promote urban livability and sustainability.

The coronavirus disease 2019 (COVID-19) pandemic has provided an opportunity to rethink the development of a sustainable and resilient city. A framework for comprehensive intracity pandemic risk evaluation using mobile phone data is proposed in this study. Four steps were included in the framework: identification of high-risk groups, calculation of dynamic population flow and construction of a human mobility network, exposure and transmission risk assessment, and pandemic prevention guidelines. First, high-risk groups were extracted from mobile phone data based on multi-day activity chains. Second, daily human mobility networks were created by aggregating population and origin-destination (OD) flows. Third, clustering analysis, time series analysis, and network analysis were employed to evaluate pandemic risk. Finally, several solutions are proposed to control the pandemic. The outbreak period of COVID-19 in Shanghai was used to verify the proposed framework and methodology. The results show that the evaluation method is able to reflect the different spatiotemporal patterns of pandemic risk. The proposed framework and methodology may help prevent future public health emergencies and localized epidemics from evolving into global pandemics.

Dockless bicycle-sharing is one of the novel transportation modes that emerged in recent years. As a component of the public transit system, it plays an important part in connecting the urban metro system but also generates some problems. With the low parking turnover rate, the oversupply of bicycles will generate parking demand and occupy a large amount of urban public land. To make the dockless bicycle-sharing service a more effective travel mode for connecting the metro system, it is necessary to implement policy zoning with different management strategies for efficient land utilization in the dockless bicycle-sharing system. In this paper, the bicycle-sharing trips data and the metro trips data are utilized to extract indexes to assess the connection performance and operation potential between the bicycle-sharing system and metro transit system. A methodology of coverage area division and policy zoning based on cycling attraction area areas is used. Applying the methodology proposed to a case study in Shanghai, the metro stations in Shanghai are classified into four clusters with different characteristics, including stations with effective land utilization, stations with the potential to promote BS service, stations with deficiencies in BS service, and stations with the potential to improve the land utilization efficiency. According to the different demand patterns and operation performance of the four clusters, corresponding policy suggestions are proposed.

Dockless bicycle-sharing (DLBS) is one of the novel transportation modes emerging in recent years. As a newly arisen mode, dockless bicycle-sharing inevitably has influence on the existing components of the public transportation system, especially the metro system. A large number of scholars have explored the integration relationship between the two. However, through the evaluation and quantification of the dockless bicycle-sharing data and the metro automatic fare collection data, we find that the relationship between the two is not unique. Based on the location of origin and destination, the travel duration, and the travel distance, the dockless bicycle-sharing trips closely related to the metro were identified and categorized into three different temporal-spatial relationships: competition trips, connection trips, and complementation trips. Three indicators were proposed to characterize the relationship between the two systems. A case study was carried out in Shanghai, China. The proposed method was applied to investigate when, where, and to what extent the dockless bicycle-sharing trips compete with, integrate with, and complement the metro. The results show that dockless bicycle-sharing mainly integrates with and complements the metro. It is where the dockless bicycle-sharing trip takes place and the trip significantly determines its relationship with the metro. The findings provide significant implications regarding the design and management of dockless bicycle-sharing and the metro.

In Chinese cities, the widespread problem of the low density of the road network has seriously damaged the convenience of pedestrian crossing, resulting in an unfriendly pedestrian experience and restricted development of non-motorized traffic within the city. Only by accurately capturing the crossing needs of pedestrians can we adopt a targeted approach to improve the pedestrian crossing experience. In this paper, the demand and supply are considered synthetically, and a method of using point of interest (POI) data to analyze the demand for pedestrian crossing facilities at the mid-block is proposed. First, we developed the method of calculating the pedestrian crossing demand intensity based on POI data. Secondly, based on the appropriate length threshold and pedestrian crossing demand intensity threshold, a series of road sections with strong demand for pedestrian crossing facilities are identified in the study area. Finally, we use mobile phone data to obtain the intensity of residents’ activity in different areas, and find that the distribution of the areas with more activity is basically the same as that of the target road sections. The result shows that the method proposed in this paper can effectively identify the road sections with strong demand for crossing facilities at mid-block, and can provide support for the improvement of urban non-motorized traffic.

Urban traffic congestion is one of the urban diseases that needs to be solved urgently. Research has already found that a few road segments can significantly influence the overall operation of the road network. Traditional congestion mitigation strategies mainly focus on the topological structure and the transport performance of each single key road segment. However, the propagation characteristics of congestion indicate that the interaction between road segments and the correlation between travel speed and traffic volume should also be considered. The definition is proposed for “key road cluster” as a group of road segments with strong correlation and spatial compactness. A methodology is proposed to identify key road clusters in the network and understand the operating characteristics of key road clusters. Considering the correlation between travel speed and traffic volume, a unidirectional-weighted correlation network is constructed. The community detection algorithm is applied to partition road segments into key road clusters. Three indexes are used to evaluate and describe the characteristic of these road clusters, including sensitivity, importance, and IS. A case study is carried out using taxi GPS data of Shanghai, China, from May 1 to 17, 2019. A total of 44 key road clusters are identified in the road network. According to their spatial distribution patterns, these key road clusters can be classified into three types—along with network skeletons, around transportation hubs, and near bridges. The methodology unveils the mechanism of congestion formation and propagation, which can offer significant support for traffic management.