Lijun Chen

The coordinated development of township and city transportation is expected to reach new heights in the global sustainable transport plans of emerging economies. However, few studies have focused on the transport hub features considering marginal administrative division. This study examines the correlation between township development and hub level by using remote sensing of nightlight imagery. Systematically corrected satellite images of Global NPP-VIIRS Nighttime lights were selected as experimental data. Furthermore, the township hub level model and nighttime light indices were established to demonstrate the correlation characteristics of 6671 townships. Results show that the development level of road transport for a considerable number of townships is positively correlated with the hub level. The positively correlated townships show a spatial clustering distribution. In contrast, several negative correlations and random townships are related to the radiation of adjacent city growth poles and township special industrial characteristics. Nighttime light data can compensate for the difficulty in obtaining socioeconomic data below the prefecture level from a multiscale micro perspective and statistical caliber differences. These findings can be proven to be valuable to planners and designers of township development and regional transport.

Transfer terminals with a high integration level could provide favorable support to Mobility-as-a-Service, which is essential to sustainable transportation. The interconnection of multiple transfer facilities in terminals would affect travelers’ transfer choices during their trips. Existing research on intermodality lacks an efficient multi-dimension indicator system regarding transfer facilities. To date, the selection of representative indicator systems and the construction of quantitative evaluation models based on these indicators that can reflect the coupling relationship between transfer facilities effectively and comprehensively are still challenging. Aiming at this challenge, this study conducts the work in the following three ways. First, the concept of the degree of coupling coordination of multiple transfer facilities (DCC-MTF) is proposed to evaluate the interaction among multiple transfer facilities. Second, indicators of diversity, selectivity, accessibility, and continuity are designed and used to establish the model by using the entropy weight method. Third, the spatial distribution characteristics of DCC-MTF for each transfer facility are geo-visualized and clustered. Field observation is conducted at Lelylaan Station in the city of Amsterdam, and the indicator analysis derived from the collected data is discussed. Overall, the quantitative evaluation model presented in this study, which is universal and can be applied to similar scenarios, is directly valuable for planners and designers who aim to improve the integration level of transfer terminals.

Cycling rhythm performance is the result of a complex interplay between active travel demand and cycling network supply. Most studies focused on bicycle flow, but little attention has been paid to cycling rhythm changes for public bicycles. Full sample data of origin–destination enables an efficient description of network-wide cycling mobility efficiency in urban public bicycle systems. In this paper, we show how the spatiotemporal characteristics of cycling speed reveal the performance of cycling rhythms. The inference method of riding speed estimation is proposed with an unknown cycling path. The significant inconsistency of docking stations in cycling rhythm was unraveled by the source–sink relationship comparison. The asymmetry of the cycling rhythm on the path is manifested as the rhythm difference among paths and bidirectional inconsistency. We found that cycling rhythm has a temporal multilayer and spatial mismatch, which shows the inflection points of the cycling rhythm where the travel behavioral preference changes and the exact road segments with different rhythms. This finding suggests that a well-designed cycling environment and occupation-residential function should be considered in active transport demand management and urban planning to help induce active travel behavior decisions.

The effect of the plum rain weather event on cycling trips reflects the climate resilience of the public bicycle system. However, quantitative studies regarding the impact of plum rain on public bicycle users and corresponding spatial heterogeneity have not been paid much attention. This paper explores the spatial pattern of affected levels from the perspective of cyclist number, place semantics and riding distance. Corresponding public bicycle trips in normal weather are predicted by spatial-temporal random forest prediction. GIS neighborhood statistics and clustering algorithms are adapted to analyze and visualize the affected levels using origin-destination data of public bicycle trips and point of interest data of city public facilities. It is proved that there is an obvious spatial difference in affected levels by plum rain from three dimensions. In the dimension of the number of cyclists, the docking stations with different affected levels are distributed across the whole urban area. In the place semantic dimension, the docking stations with high affected levels show a clustered zonal distribution in the city center. In the dimension of cycling distance, the docking stations with high affected levels are mainly distributed in the periphery of the central urban area. The study theoretically expands the impact mechanism of environment and active transport. It is beneficial for the early monitoring, warning and assessment of climate change risks for public bicycle planning and management.