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

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