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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]

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]

This paper provides the first evidence of the causal effect of COVID-19 on metro use using real-time data from the Taipei Metro System in Taiwan. In contrast to other cities or countries, Taiwan did not enforce strict social lockdowns or mandatory stay-at-home orders to combat COVID-19. The major prevention strategies to the pandemic in Taiwan include promoting social distancing, mandating the wearing of face masks in public areas, and requiring all international arrivals to quarantine for 14 days. Using administrative data on confirmed cases of COVID-19 and ridership from metro stations with the difference-in-differences model, we find that an additional new confirmed case of COVID-19 reduces metro use by 1.43% after controlling for local socio-demographic variables associated with ridership and the number of international arrivals to Taiwan. This result implies that the reduction in metro trips is attributable to decreases in residents' use of public transportation due to perceived health risks. Furthermore, the effect of COVID-19 on metro use disproportionally impacts stations with different characteristics. The effect is more pronounced for metro stations connected to night markets, shopping centers, or colleges. Although decreases in metro ridership lower the revenue of the Taipei Metro System, our results indicate a tradeoff between increased financial burdens of public transportation systems and reducing medical expenses associated with COVID-19. [ABSTRACT FROM AUTHOR]