Many countries have implemented public bike systems to promote sustainable public transportation. Despite the rapid development of such systems, few studies have investigated how built environment factors affect the use of public bikes at station level using trip data, taking account of the spatial correlation between nearby stations. Built environment factors are strongly associated with travel demand and play an important role in the success of public bike systems. Using trip data from Zhongshan's public bike system, this paper employed a multiple linear regression model to examine the influence of built environment variables on trip demand as well as on the ratio of demand to supply (D/S) at bike stations. It also considered the spatial correlations of PBS usage between nearby stations, using the spatial weighted matrix. These built environment variables mainly refer to station attributes and accessibility, cycling infrastructure, public transport facilities, and land use characteristics. Generally, we found that both trip demand and the ratio of demand to supply at bike stations were positively influenced by population density, length of bike lanes and branch roads, and diverse land-use types near the station, and were negatively influenced by the distance to city center and the number of other nearby stations. However, public transport facilities do not show a significant impact on both demand and D/S at stations, which might be attributed to local modal split. We also found that the PBS usage at stations is positively associated with usage at nearby stations. Model results also suggest that adding a new station (with empty capacity) within a 300 m catchment of a station to share the capacity of the bike station can improve the demand-supply ratio at the station. Referring to both trip demand models and D/S models, regression fits were quite strong with larger R 2 for weekdays than for weekends and holidays, and for morning and evening peak hours than for off-peak hours. These quantitative analyses and findings can be beneficial to urban planners and operators to improve the demand and turnover of public bikes at bike stations, and to expand or build public bike systems in the future.

In the pursuit of sustainable mobility policy makers are giving more attention to cycling. The potential of cycling is shown in countries like the Netherlands, where cycling covers 25 % of all person trips. However, the effect of policy interventions on cycling demand is difficult to measure, not least caused by difficulties to control for changing context variables like weather conditions. According to several authors weather has a strong influence on cycling demand, but quantitative studies about the relationship are scarce. We therefore further explored this relationship, with the aim of contributing to the development of a generic demand model with which trend and coincidence in bicycle flows might be unraveled. The study is based on time-series between 1987 and 2003 of daily bicycle flows, collected on 16 cycle paths near two cities in the Netherlands. The regression analyses show that, not surprisingly, recreational demand is much more sensitive to weather than utilitarian demand. Most daily fluctuations (80 %) are described by weather conditions, and no less than 70 % of the remaining variation is locally constrained. The regression can therefore mainly be improved by incorporating path specific, as yet unknown, variables. We used the regression results to calculate weather-inclusive bicycle flow predictions and found indications of a downward trend in recreational demand. This trend has been off-set in the observed flows by more favorable weather conditions over the years considered.

Congestion is increasing in many urban areas. This has led to a growing awareness of the importance of accurate traffic-flow predictions. In this paper, we introduce a prediction scheme that is based on an extensive study of volume patterns that were collected at about 20 urban intersections in the city of Almelo, The Netherlands. The scheme can be used for both shortand long-term predictions. It consists of 1) baseline predictions for a given preselected day, 2) predictions for the next 24 h, and 3) short-term predictions with horizons smaller than 80 min. We show that the predictions significantly improve when we adopt some straightforward assumptions about the correlations between and the noise levels within volumes. We conclude that 24-h predictions are much more accurate than baseline predictions and that errors in short-term predictions are even negligibly small during working days. We used a heuristic approach to optimize the model. As a consequence, our model is quite simple so that it can easily be used for practical applications.

This study aims to explore the travel behavior of bike-sharing users in Zhongshan, China. To this end, we use 5 months of trip data, which included origin and destination locations, and pickup and return time of each used bike in the system. To get a complete picture of the behavior, we distinguished between trips, trip chains, and transition activities. We categorized different trip chains and constructed transition matrices between activities. We found that almost all trips have different origin and destination stations. Two thirds of the trips are part of a trip chain consisting of multiple trips. Although users often use another station to start their next trip, a clear picture emerges in which public bikes are used as a single mode to hop from one destination to another, and at the end return more or less to the same location where the trip chain was started. Moreover, based on the trip chain matrices and transition matrices between activities, we conclude that users mainly used public bikes for commuting, and some of users went home during lunch break, while the system was also used or after-work shopping activities.

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