Level of smartness and technology readiness of bicycle technologies affecting cycling safety: A review of literature

Unlike motor-vehicle transport, the implementation of lnformation and Communications Technologies (ICT) and Cooperative Intelligent Transport Systems (C-ITS) in cycling has not been comprehensively investigated [1]. Cycling offers several benefits both to society and the environment and is one of the most sustainable and green transportation modes [2]. Many people worldwide have been switching to bicycles during the last decades, and this has increased even more due to the Covid pandemic [3]. Furthermore, the number of people who ride an e-bike is also rising [ 4]. Thus, the number of cyclists is increasing and, in turn, the number of cycling accidents is increasing too. For instance, in the Netherlands, one of the most cycling-friendly countries, 31 % of all road fatalities in 2019 were cyclists (203 fatalities), while in 2020, it was 37% (229 fatalities). One-third of these fatalities were e-bike users [5]. Despite the constantly evolving landscape of cycling and electric bike adoption, applications of new technologies in bicycles are still immature. In recent years, academic research on new technologies related to cyclists' comfort and safety is growing [6, 7, 8]. Furthermore, many studies focus on technologies affecting cyclists' road safety; however, it is unclear what type of technologies are implemented for bicycles. To the best of the authors' knowledge, a comprehensive review of such studies is lacking. Additionally, a clear definition of a 'smart bike'- a concept gaining popularity nowadays, is missing in the literature. To address this gap, the objective of this paper is two fold: 1) to review the state-of-the-art technologies implemented in bicycles to improve cyclists' safety, and 2) to propose an original classification for the levels of smartness of newly emerging 'smart bikes'

Effects of crowding on route preferences and perceived safety of urban cyclists in the Netherlands

Bicycle use increases in many cities around the world. In the Netherlands, cycling is one of the main transport modes in cities and bicycle use is still growing. This leads to crowded cycling infrastructure in cities with high cycling shares, including in the four largest Dutch cities. Since few studies have been done to the effects of high crowding levels on cyclists’ route preferences and perceived safety, the present study aims to examine this for Dutch urban cyclists. Moreover, the relationship between perceived safety and route preferences is established. To investigate this, a questionnaire, including a route choice experiment, is completed by 1,329 cyclists from the four largest Dutch cities. The effects of varying crowding levels on route preferences and perceived safety are analysed with Mixed Logit models. Logistic regression is used to investigate the consistency between route preferences and perceived safety. The results show that crowding negatively affects route preferences as well as perceived safety, and that the impact is stronger for older cyclists and women. Furthermore, high crowding levels have a negative impact on the preference for and perception of safety of cycling infrastructure. Moreover, it is shown that all investigated route attributes have a significant effect on perceived safety, implying a more direct relationship between perceived safety and route preferences. In addition, the results show that most cyclists prefer routes they also perceive as safe. Concludingly, crowding seems an important issue for cyclists in large Dutch cities. Moreover, the perception of safety is likely to increase with the implementation of cycling infrastructure suitable for large flows of cyclists, leading to a safer cycling network for all types of cyclists

The effects of hourly variation in exposure to cyclists and motorized vehicles on cyclist safety in a Dutch cycling capital

While cycling is promoted as a sustainable and healthy mode of transport in many eitles in the Global North [1, 2], there are increasing concerns about the safety of cyclists. The increasing bicycle use in urban areas leads to a more intensely used cycling network, resulting in safety risks for cyclists [3]. Since 2010, the number of bicycle fatalities stagnated and the number of severely injured cyclists increased by 28% until 2018 in the European Union [4]. lt is therefore necessary to examine how bicycle use and motorized vehicle use in cities affects the nunber of bicycle crashes. To investigate this, the effect of the network-wide hourly exposure to cyclists and motorized vehicles on bicycle crash frequency is examined. That is, the total number of cyclists and motorized vehicles in the whole road network for each hour of the week were estimated and used as the network-wide hourly exposure. This approach allowed us to capture safety impacts of temporal variation in the numbers of cyclists and motorized vehicles in the same network more accurately. lt is a different approach compared to most bicycle safety studies, which often only use the daily average of bicycle and motorized vehicle volumes. The work presented here is based on our publication in Safety Science [5]

Analyzing network-wide patterns of rail transit delays using Bayesian network learning

Rail transit delays are generally discussed in terms of on-time performance or problems at individual stops. Such stop-scale approaches ignore the fact that delays are also caused and perpetuated by network-wide factors (e.g., bottlenecks caused by shared tracks by multiple transit lines). The objective of this paper is to develop a network model and metrics that can quantify the delay dependencies between transit network stops, and identify local sources of network-wide issues. For this purpose, Bayesian network learning (at the intersection of machine learning and network science) was utilized. Based on the calculated Bayesian networks (BNs), network metrics (inducer and susceptible) were formulated to quantify the network-wide impacts of the delays experienced at the stops. To implement the proposed framework, the delays at Long Island Rail Road (LIRR) were gathered through a crowdsourced real-time transit information app called onTime. The developed BN model was tested through cross-validation, yielded promising accuracy results, successfully identified the problematic stops based on LIRR reports, and provided further insights on network impacts. The BN model and the developed metrics were further tested using a natural experiment, i.e., a before and after study focusing on a recently completed track expansion project at LIRR. The findings imply that BN learning can successfully identify the network dependencies and indicate the rail links/corridors that are the best candidate for subsequent improvement investments. Overall, the developed metrics can quantify the delay dependencies between stops and they can be used by policy makers and practitioners for investment and improvement decisions

Value of convenience for taxi trips in New York City

The alternative public transportation options such as subway, bus, or taxis compete with each other to attract passengers. The competition depends on many factors such as travel time, reliability and convenience. Convenience is a collection of attributes affecting the attractiveness of the service including access and egress easiness, service frequency, crowding, comfort and information availability. It can be argued that the taxi preference when there is viable public transportation option is associated with the perceived convenience of taxis. The objective of this study is to evaluate the value associated with convenience of taxis in New York City by utilizing the large taxi trip data. First, the taxi trips which could be replaced by subway without any access or service availability issues (e.g., no transfers between subway lines) are extracted. Then, a multilevel modeling approach was utilized to estimate the monetary value associated with taxi convenience were estimated for different day-of-week and time-of-day periods, and areas in Manhattan. The results show that the value of convenience varies depending on the ratio of taxi travel time to subway travel time, and occasionally intersect when the ratio is close to 1. Furthermore, the corresponding value of convenience (VC) at those points (i.e., taxi travel time is equal to subway travel time) is close to $32/hr for all the zones during weekdays and weekends. Results also indicate that value of time is generally higher at peak hours during weekdays, whereas it is lower during weekend and social period at night and early morning hours

Level of smartness and technology readiness of bicycle technologies affecting cycling safety: A review of literature

Unlike motor-vehicle transport, the implementation of lnformation and Communications Technologies (ICT) and Cooperative Intelligent Transport Systems (C-ITS) in cycling has not been comprehensively investigated [1]. Cycling offers several benefits both to society and the environment and is one of the most sustainable and green transportation modes [2]. Many people worldwide have been switching to bicycles during the last decades, and this has increased even more due to the Covid pandemic [3]. Furthermore, the number of people who ride an e-bike is also rising [ 4]. Thus, the number of cyclists is increasing and, in turn, the number of cycling accidents is increasing too. For instance, in the Netherlands, one of the most cycling-friendly countries, 31 % of all road fatalities in 2019 were cyclists (203 fatalities), while in 2020, it was 37% (229 fatalities). One-third of these fatalities were e-bike users [5]. Despite the constantly evolving landscape of cycling and electric bike adoption, applications of new technologies in bicycles are still immature. In recent years, academic research on new technologies related to cyclists' comfort and safety is growing [6, 7, 8]. Furthermore, many studies focus on technologies affecting cyclists' road safety; however, it is unclear what type of technologies are implemented for bicycles. To the best of the authors' knowledge, a comprehensive review of such studies is lacking. Additionally, a clear definition of a 'smart bike'- a concept gaining popularity nowadays, is missing in the literature. To address this gap, the objective of this paper is two fold: 1) to review the state-of-the-art technologies implemented in bicycles to improve cyclists' safety, and 2) to propose an original classification for the levels of smartness of newly emerging 'smart bikes'

Crash Patterns in the COVID-19 Pandemic: The Tale of Four Florida Counties

This study investigates the impacts of the noticeable change in mobility during the COVID-19 pandemic with analyzing its impact on the spatiotemporal patterns of crashes in four demographically different counties in Florida. We employed three methods: (1) a Geographic Information System (GIS)-based method to visualize the spatial differences in crash density patterns, (2) a non-parametric method (Kruskal–Wallis) to examine whether the changes in crash densities are statistically significant, and (3) a negative binomial regression-based approach to identify the significant socio-demographic and transportation-related factors contributing to crash count decrease during COVID-19. Results confirm significant differences in crash densities during the pandemic. This may be due to maintaining social distancing protocols and curfew imposement in all four counties regardless of their sociodemographic dissimilarities. Negative binomial regression results reveal that the presence of youth populations in Leon County are highly correlated with the crash count decrease during COVID-19. Moreover, less crash count decrease in Hillsborough County U.S. Census blocks, mostly populated by the elderly, indicate that this certain age group maintained their mobility patterns, even during the pandemic. Findings have the potential to provide critical insights in dealing with safety concerns of the above-mentioned shifts in mobility patterns for demographically different areas