Estimating city-wide hourly bicycle flow using a hybrid LSTM MDN

Cycling can reduce greenhouse gas emissions and air pollution and increase public health. With this in mind, policy-makers in cities worldwide seek to improve the bicycle mode-share. However, they often struggle against the fear and the perceived riskiness of cycling. Efforts to increase the bicycle's mode-share involve many measures, one of them being the improvement of cycling safety. This requires the analysis of the factors surrounding accidents and the outcome. However, meaningful analysis of cycling safety requires accurate bicycle flow data that is generally sparse or not even available at a segment level. Therefore, safety engineers often rely on aggregated variables or calibration factors that fail to account for variations in the cycling traffic caused by external factors. This paper fills this gap by presenting a Deep Learning based approach, the Long Short-Term Memory Mixture Density Network (LSTMMDN), to estimate hourly bicycle flow in Copenhagen, conditional on weather, temporal and road conditions at the segment level. This method addresses the shortcomings in the calibration factor method and results in 66-77\% more accurate bicycle traffic estimates. To quantify the impact of more accurate bicycle traffic estimates in cycling safety analysis, we estimate bicycle crash risk models to evaluate bicycle crashes in Copenhagen. The models are identical except for the exposure variables being used. One model is estimated using the LSTMMDN estimates, one using the calibration-based estimates, and one using yearly mean traffic estimates. The results show that investing in more advanced methods for obtaining bicycle volume estimates can benefit the quality, mitigating efforts by improving safety analyses and other performance measures

Statistical modelling of cycling accidents:Investigating Risk, Severity and Consequences

Increasing the mode-share of the bicycle poses a solution to many issues in modern society. It has the potential to alleviate urban congestion, reduce noise and air pollution (NOx and PM25) while also providing health benefits for the individual users through increased physical activity. However, the safety of cyclists in traffic remains a major concern. Bicyclists are considerably more vulnerable in traffic than most other road users. Thus, increasing the cycling mode share might have undesired impacts on road safety as the number of cycling injuries may increase. To address the current safety concerns regarding cycling and ensure the future attractiveness of cycling for transport, informed decisions concerning safety measures and preventive efforts to promote cycling safety are necessary. These informed decisions rely on a thorough understanding of the factors related to bicycle crashes and their potential outcomes, not to mention the subsequent experiences. This PhD thesis concerns several aspects of modelling and analysing bicycle crashes to supply new methodologies and findings to support future mitigating efforts and promote cycling safety. The work of this thesis is split into four parts: Part I concerns the use of model-based approaches to improve the current standard for the estimation of disaggregated bicycle exposure, which is essential to the investigation of cycling crashes. Part II concerns the risk assessment that is possible when presented with disaggregated exposure data. Following this, Part III covers the estimation of the injury severity of bicycle crashes. Finally, Part IV deals with health consequences reported by cyclists having suffered bicycle crashes.Information concerning bicycle exposure is crucial to analysing factors influencing bicycle crashes. Therefore, the first part of the thesis addresses the issue of accurately estimating bicycle exposure. In this part of the thesis, the importance of having accurate measures of bicycle exposure to improve cycling safety is highlighted. Meanwhile, bicycle traffic data at the segment level is sparse and seldomly available. This issue leads many studies to either leave out exposure variables or use aggregated variables. To alleviate this, the paper in Part I presents a novel deep-learning method to estimate hourly bicycle flow in anetwork conditional on mean cycling flow, weather and time. The model presents a superior alternative to the calibration-factor method applied by the Danish Road Directorate, yielding significantly more accurate cycling flow estimates. Furthermore, bicycle crash frequency models are estimated using cycling flow estimates of varying degrees of disaggregation and quality to quantify the impact of improved exposure data in crash analyses.This comparison provides statistical evidence for improved model accuracy given better exposure data. The second part of the thesis investigates the analysis and inference concerning bicycle crash risk that is possible when presented with disaggregate cycling flow data. The paper presented in this part deals with the bicycle crash risk assessment in Copenhagen through a study that applies a recently developed disaggregated and non-parametric method for crash risk evaluation. The method presents researchers with a flexible framework for crash risk analysis that compares the distribution of conditions as seen by an arbitrary cyclist (the Palm distribution) with those seen by a cyclist subject to a crash (the accident distribution). The paper uses this Palm distribution method to reveal the relative changes in bicycle crash risk given adverse weather and time conditions. The study reveals several findings concerning the relation between weather conditions and the bicycle crash risk. Furthermore, the disaggregate risk assessment approach identifies correlational patterns that disappear with aggregation.The third part of the thesis investigates the injury severity outcomes of bicycle crashes seeking to identify circumstances around crashes statistically associated with the resulting injury severity. The paper presented in this part specifically investigates the factors influencing the injury severity of single-bicycle crashes. These crashes are of special interest due to their high prevalence, reported to make up more than half of all bicycle crashes. Also, with the efforts to increase the bicycle mode share, this crash type is expected to increase. Nevertheless, the factors that influence the injury severity in single-bicycle crashesare still rather unexplored. The analysis relies on a Latent Class Ordered Probit model estimated using hospital data on bicycle crashes combined with infrastructure information for the time and place of the respective crashes. By allowing the probabilistic class assignment to vary according to cyclists’ age and gender, this study yields beneficial insight into the behavioural groups of crashed cyclists. Furthermore, the study provides valuable results concerning the impact of bicycle-specific infrastructure, and its maintenance, on injuries.The final part of the thesis examines the possible health consequences of suffering bicycle crashes with respect to the injuries and non-injury factors. The paper presented in this section addresses the issue by investigating distress symptoms reported by cyclists and their relation to potential crash involvement. The paper first investigates the frequency of reported distress symptoms, comparing the reported frequency among crashed cyclists with a control group of non-crash cyclists. Secondly, structural equation modelling (SEM) is applied to identify underlying distress constructs and investigate their relation to injuriesand non-injury factors. The results show that the crashed cyclists report fewer distress symptoms than the non-crash cyclists. Only crashed cyclists who considered their crashes severe reported more distress symptoms than the non-crash cyclists. Three latent distress constructs are identified: ”General stress & exhaustion”, ”Depression & anxiety”, and ”Physical impairment”. The SEM reveals several injuries positively associated with the distress structures and highlights the importance of the lower-extremity injuries to cyclists’ health. Furthermore, several non-injury factors are significantly associated with the latentdistress constructs. Thereby, highlighting the importance of accounting for non-injury factors when assessing the health consequences of crashes. Lastly, the strong relation between ”Depression & anxiety” and a poorer perceived quality of life among the crashed cyclists signifies the importance of considering the potential psychological and mental aspects of suffering a crash.In summary, this PhD has contributed to the research on bicycle crash modelling and the investigation of crash consequences, covering topics related to cycling exposure and the impact on accident analysis, bicycle crash risk, injury severity outcomes, and distress following bicycle crashes. Through the development and application of novel methodologies and data in the context of Danish cycling, this thesis seeks to provide practitioners, researchers, and policymakers alike with tools and findings relevant for the continued promotion of cycling safety. Specifically the results from this thesis suggest a great need for better bicycle traffic estimation methods to overcome the lack of cycling monitoring. The thesis contributes important knowledge concerning the impact that better bicycle traffic data can have on cycling safety analysis, adding that accurate cycling flow estimates are paramount to accurate analysis of crash factors. Furthermore the thesis exemplifies the detailed analysis of crash factors that is enabled through the improved cycling flow estimates in an analysis assessing the relative risk of bicycle crashes in adverse weather conditions. This analysis contributes findings of previously unexplored correlations such as the effects of impaired visibility on the bicycle crash risk. Regarding the injury severity outcome of bicycle crashes, this thesis presents new knowledge on the factors influencing the injury severity of single-bicycle crashes. It specifically highlights the importance of bicycle lanes and their maintenance to mitigate the severity of single-bicycle crashes. Finally, the thesis expands the knowledge of the health impacts of bicycling and crashes, providing additional evidence of the complex interactions of the health benefits from cycling and the adverse health effects of crashing. Furthermore it presents findings revealing that both injuries and demographic factors influence various aspects of cyclists health after crashing, and shows how these aspects relate to their perceived quality of life

Cykling og Helbred: udvidet resumé

Cykling og aktiv transport har en række samfundsmæssige og personlige fordele i form af mindsket risiko for en række af sygdomme. Indsigt i personers egen opfattelse af livskvalitet og helbred i sammenhæng med cykling som aktiv transport er relevant for at øge antallet af cyklister og cyklende pendlere. Baseret på en spørgeskemaundersøgelse med 337 deltagere, ses der i denne undersøgelse på sammenhængen mellem cykelvaner og cykeluheld og rapporteret livskvalitet og tilfredshed med eget helbred. Undersøgelsen viser, at personer der cykler mere end fem gange om ugen giver udtryk for signifikant større helbredstilfredshed og livskvalitet end personer, der cykler én eller færre gange om ugen. Ligeledes viser undersøgelsen også en signifikant sammenhæng mellem øget ugentlig cykeldistance og større helbredstilfredshed og livskvalitet. Analysen viser ingen signifikant sammenhæng mellem helbredstilfredshed og livskvalitet og det at have været involveret i ét eller flere cykeluheld som cyklist i løbet af de seneste to år. Samlet peger resultaterne på, at folk der cykler opnår både målbare fysiske fordele og opfatter ligeledes en højere livskvalitet såvel som øget tilfredshed med deres eget helbred

Risikovurdering af trafik- og vejrforhold for cykeluheld: udvidet resumé

Dette studie analyserer faktorer der er associeret med cykeluheld ved brug af Palm teori for trafikforhold. Metoden tillader os at sammenligne af vejr- og trafikforhold set fra en arbitrær cyklists synspunkt (Palmfordelingen) og sammenligne det med dem set fra synspunktet af en cyklist i et cykeluheld (uheldsfordelingen). Det muliggør en ukompliceret måde at vurdere den relative risikoændring givet bestemte vejr- og trafikforhold, såvel som at evaluere deres signifikans. Studiet er baseret på uheldsdata (1136 cykeluheld) fra Københavns- og Frederiksberg kommune samlet over en periode på fire år. Den relative risikoændring blev vurderet pa basis af tid, vejr og sæson. Relative risikoforøgelser blev identificeret som værende signifikante i nat perioder (0-4) såvel som under morgen- og eftermiddags myldretid. Ved ydereligere analyse viste det sig dog, at natteperioder kun viste sig at være relateret til signifikant risikoforøgelse i weekenderne og morgen- og eftermiddags myldretiden, kun i hverdagene. Dette viser samtidigt, hvordan brugen af over-aggregerede forklarende variable kan lede til misvisende konklusioner når det kommer til potentielle interventions. Yderligere viste resultaterne, at nedbør var relateret til en forøgelse af risikoen for cykeluheld. Overordnet giver Palm-distributionen en mulighed for en ny metode til at evaluere cykeluheld og identificere faktorer og forhold relateret til forsørgelsen af risiko for uheld

Assessing bicycle crash risks controlling for detailed exposure:A Copenhagen case study

A better understanding of factors associated with bicycle crashes can inform future efforts to limit crash risks. Many previous studies have analysed crash risk based on crash databases. However, these can only provide conditional information on crash risks. A few recent studies have included aggregate flow measures in their crash risk analyses. This study incorporates detailed bicycle flow to investigate factors related to bicycle crashes. Specifically, the study assesses the relative crash risk given various conditions by applying Palm distributions to control for exposure. The study specifically investigates the relationship between weather and time conditions and the relative risk of bicycle crashes at a disaggregate level. The study uses bicycle crash data from police reports of bicycle crashes from 2017–2020 in the greater Copenhagen area (N = 4877). The relations between the bicycle crash risk and the air temperature and wind speeds are found to be highly non-linear. The relative risk of bicycle crashes is elevated at low and high temperatures (0 °C ¿ x, x ¿ 21 °C). The results also show how decreasing visibility relates to increasing bicycle crash risk. Meanwhile, cycling during the early morning peak (7–8) and afternoon peak hours (15–18) is related to an increased risk of bicycle crashes. While some of the effects are likely spurious, they highlight specific conditions associated with higher relative risk. Finally, the results illustrate the increased risk at weekend night times when cyclists are likely to bike under the influence of alcohol.In conclusion, the analysis confirms that visibility, slippery surfaces, and intoxication are all factors associated with a higher risk of bicycle crashes. Hence, it is relevant to consider how infrastructure planning and preventive measures can modify the bicycle environment to minimise these risks.</div

Our children cycle less - A Danish pseudo-panel analysis

In this paper, we investigate the dynamics of cycling demand for the population of Denmark. Using pseudo-panels based on large-scale cross-sectional data, we analyse the temporal stability of cycling demand preferences for different age cohorts in combination with residential city sizes. Cycling demand is decomposed into two effects. Firstly, a population ’selection’ effect that explains the probability of being a cyclist, i.e. engaged in cycling activities. Secondly, the conditional demand for cycle mileage provided that the respondent is a cyclist. The joint probability model is estimated as a Gamma Hurdle model. The study reveals several empirical findings, of which three stand out. Firstly, overall cycling demand in Denmark over the period is in decline. Secondly, it is shown that this is mainly a selection effect. Hence, the main driver of the observed decline is essentially a shrinking cycling population rather than a decrease in trip distances for those who travel by bicycle. Thirdly, the decline is strongest for younger generations, particularly those residing outside the larger cities. With Denmark being an international forerunner for bicycling and with a cycling culture developed over many decades, we believe these findings can be relevant to mitigate similar long-term changes in other countries