Search

Most bicycle collision studies aim to identify contributing factors and calculate risks based on statistical data (Loidl et al., 2016). The aim of this paper is to follow this approach, focusing on bicycle-motorized vehicle (BMV) collisions through a spatio-temporal workflow. For the spatial dimension (Kernel Density Estimation (KDE) method), a general estimation of the collision risks was obtained and the labour-intensive work of collecting counting data was avoided on the macro-scale level. The temporal dimension (negative binomial modeling method) focused on data from collisions occurring on bridges, enabling the inclusion of traffic exposure (counting data on the micro-scale level). Bridge collision risks and contributing factors related to road environment and cycling facilities were estimated using databases from eight government authorities and field investigation. For the presented case study, 2044 geo-coded bicycle collisions in the Taipei-Capital Region (Taiwan) were analysed. The data set covers three years (2015–2017) and includes all BMV collisions reported by the police. Through the spatial workflow, urban bridges were identified as areas with the highest density of collisions. This is unsurprising given that bicycle facilities on urban bridges face design difficulties due to limited space, discrepancy in elevation and traffic volume. Through this approach the characteristics of BMV collisions on bridges, traffic engineering, road environment, traffic control system, and driving behaviour were then analysed in the temporal dimension. This paper concludes by providing information relevant to traffic engineers concerning the enhancement of bicycle safety on high-risk areas in the city. In this paper, we aim to (1) understand the risk patterns of bicycle collisions spatially (where?) and temporally (when?), from a region-scale (macro) level to a location-scale (micro) level. To this end, a spatio-temporal (two-stage) workflow was developed for the exploration of the collision data. Through the spatial stage, urban bridges were identified as having the highest density of BMV collisions. Building on results from the spatial stage, we sought to (2) further explain "how" bridge infrastructure influences bicycle collisions in Taipei-Capital Region (Taiwan) by studying contributing risk factors. Countermeasures can thus be made to enhance bicycle safety. This paper is organized as follows. Section 2 provides an overview of the literature related to bicycle collision studies. Section 3 describes the study concept of the spatio-temporal workflow and its methodologies. Section 4 presents the study area and provides the descriptive statistics of BMV collisions. Section 5 describes the data used in spatio-temporal modeling. Section 6 discusses the main results and Section 7 finally concludes with the contribution and recommendation of this research. [ABSTRACT FROM AUTHOR]

The bikeshare program in Taipei City and New Taipei City, called U-bike, was launched in August 2012 and has more than 7500 bicycles operating out of 769 stations. Research has suggested that bicycle helmet use is a means of reducing morbidity and mortality among bike users. Helmets, however, are not available for rent when a U-bike is rented. The current research conducted an observational study to examine the prevalence of helmet non-use by users of the bikeshare program, electric bicycles, racing bicycles, and personal bicycles in Taipei City and New Taipei City. Trained observers using compact video cameras collected helmet non-use data during various times of the day and on different days of the week. Observers collected data on cyclist attributes, bicycle types, and helmet use at several selected locations within Taipei City and New Taipei City. U-bike users were found to be the least likely to wear helmets. Other noteworthy findings include that violations such as phone use, red-light violations, and travelling at ≥25 km/h were associated with riding without a helmet. Male users of racing bikes tended not to wear helmets, while female users of other bicycle types were less likely to use a helmet. Carrying passengers by users of electric bikes and personal bikes was a determinant of helmet non-use. This paper concludes with a discussion and recommendations for future research. [ABSTRACT FROM AUTHOR]

Installing bikeways is an effective means to improve riding safety and the continuous usage intention of cyclists. This research proposes a bikeway network design model considering utilitarian and recreational cycling in urban built-up areas. In consideration of the concerns of bikers, other road users, and planners, the proposed model aims to maximize accessibility and comfort, and to minimize risk and impact. Model constraints are budget limits, connectivity among nodes and links, bikeway capacity limits, flow conservations of nodes, and value ranges of decision variables. Decisions of the model include determining which road links should be equipped with bikeways, what type of bikeway should be used, and how many lanes should be installed for a bikeway link. A case study in Taipei City, Taiwan is conducted to verify the model's applicability and effectiveness. Therefore, the proposed model is a novel bikeway network design model that considers utilitarian and recreational cycling in urban built-up areas to simultaneously determine the optimum locations, types, and lane numbers of bikeways. [ABSTRACT FROM AUTHOR]

In urban areas, public transport can improve sustainable transport by reducing vehicles and congestion, and improving accessibility. Mass rapid transit (MRT) is especially important for large cities, such as Taipei City, Taiwan. In particular, MRT aims to improve mobility and reliability, but has limitations in providing first and last mile accessibility to final destinations or origins. Taipei City, Taiwan has introduced a public bike sharing scheme to service this gap. However, few studies have addressed how a public bike sharing scheme assists first and last mile accessibility. This study uses Taipei City as a case study to investigate this issue by comparing the demand and supply of the public bike sharing scheme, YouBike, at a detailed spatial scale. The supply of YouBike is represented by the time saving compared to walking for each identified origin and destination pair in the study area (i.e., Point of Interests (POIs) and MRT stations). The demand for YouBike is total trips from each YouBike station to a MRT station using public transport smart card data. By comparing the demand and supply for over 400 zones or villages, service gaps and areas of unbalanced service can be identified. The results show that YouBike does provide first and last mile service for the MRT network with some evidence of service mismatch in the study area, i.e., high service for low demand and vice versa. The conclusions of the paper can help cities wanting to introduce a bike sharing schemes to improve first and last mile transport. [ABSTRACT FROM AUTHOR]