High-Resolution Road Vehicle Collision Prediction for the City of Montreal

Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, Canada, to create high-resolution accident prediction models, using big data analytics. Compared to other studies in road accident prediction, we have a much higher prediction resolution, i.e., our models predict the occurrence of an accident within an hour, on road segments defined by intersections. Such models could be used in the context of road accident prevention, but also to identify key factors that can lead to a road accident, and consequently, help elaborate new policies. We tested various machine learning methods to deal with the severe class imbalance inherent to accident prediction problems. In particular, we implemented the Balanced Random Forest algorithm, a variant of the Random Forest machine learning algorithm in Apache Spark. Interestingly, we found that in our case, Balanced Random Forest does not perform significantly better than Random Forest. Experimental results show that 85% of road vehicle collisions are detected by our model with a false positive rate of 13%. The examples identified as positive are likely to correspond to high-risk situations. In addition, we identify the most important predictors of vehicle collisions for the area of Montreal: the count of accidents on the same road segment during previous years, the temperature, the day of the year, the hour and the visibility

An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio

Seeing the invisible: from imagined to virtual urban landscapes

Urban ecosystems consist of infrastructure features working together to provide services for inhabitants. Infrastructure functions akin to an ecosystem, having dynamic relationships and interdependencies. However, with age, urban infrastructure can deteriorate and stop functioning. Additional pressures on infrastructure include urbanizing populations and a changing climate that exposes vulnerabilities. To manage the urban infrastructure ecosystem in a modernizing world, urban planners need to integrate a coordinated management plan for these co-located and dependent infrastructure features. To implement such a management practice, an improved method for communicating how these infrastructure features interact is needed. This study aims to define urban infrastructure as a system, identify the systematic barriers preventing implementation of a more coordinated management model, and develop a virtual reality tool to provide visualization of the spatial system dynamics of urban infrastructure. Data was collected from a stakeholder workshop that highlighted a lack of appreciation for the system dynamics of urban infrastructure. An urban ecology VR model was created to highlight the interconnectedness of infrastructure features. VR proved to be useful for communicating spatial information to urban stakeholders about the complexities of infrastructure ecology and the interactions between infrastructure features.https://doi.org/10.1016/j.cities.2019.102559Published versio

Proactive Assessment of Accident Risk to Improve Safety on a System of Freeways, Research Report 11-15

This report describes the development and evaluation of real-time crash risk-assessment models for four freeway corridors: U.S. Route 101 NB (northbound) and SB (southbound) and Interstate 880 NB and SB. Crash data for these freeway segments for the 16-month period from January 2010 through April 2011 are used to link historical crash occurrences with real-time traffic patterns observed through loop-detector data. \u27The crash risk-assessment models are based on a binary classification approach (crash and non-crash outcomes), with traffic parameters measured at surrounding vehicle detection station (VDS) locations as the independent variables. The analysis techniques used in this study are logistic regression and classification trees. Prior to developing the models, some data-related issues such as data cleaning and aggregation were addressed. The modeling efforts revealed that the turbulence resulting from speed variation is significantly associated with crash risk on the U.S. 101 NB corridor. The models estimated with data from U.S. 101 NB were evaluated on the basis of their classification performance, not only on U.S. 101 NB, but also on the other three freeway segments for transferability assessment. It was found that the predictive model derived from one freeway can be readily applied to other freeways, although the classification performance decreases. The models that transfer best to other roadways were determined to be those that use the least number of VDSs–that is, those that use one upstream or downstream station rather than two or three.\ The classification accuracy of the models is discussed in terms of how the models can be used for real-time crash risk assessment. The models can be applied to developing and testing variable speed limits (VSLs) and ramp-metering strategies that proactively attempt to reduce crash risk

Effects of Orientations, Aspect Ratios, Pavement Materials and Vegetation Elements on Thermal Stress inside Typical Urban Canyons

The analysis of local climate conditions to test artificial urban boundaries and related climate hazards through modelling tools should become a common practice to inform public authorities about the benefits of planning alternatives. Different finishing materials and sheltering objects within urban canyons (UCs) can be tested, predicted and compared through quantitative and qualitative understanding of the relationships between the microclimatic environment and subjective thermal assessment. This process can work as support planning instrument in the early design phases as has been done in this study that aims to analyze the thermal stress within typical UCs of Bilbao (Spain) in summertime through the evaluation of Physiologically Equivalent Temperature using ENVI-met. The UCs are characterized by different orientations, height-to-width aspect ratios, pavement materials, trees’ dimensions and planting pattern. Firstly, the current situation was analyzed; secondly, the effects of asphalt and red brick stones as streets’ pavement materials were compared; thirdly, the benefits of vegetation elements were tested. The analysis demonstrated that orientation and aspect ratio strongly affect the magnitude and duration of the thermal peaks at pedestrian level; while the vegetation elements improve the thermal comfort up to two thermophysiological assessment classes. The outcomes of this study, were transferred and visualized into green planning recommendations for new and consolidated urban areas in Bilbao.The work leading to these results has received funding from COST Action TU0902, the European Community’s Seventh Framework Programme under Grant Agreement No. 308497, Project RAMSES—Reconciling Adaptation, Mitigation and Sustainable Development for Cities (2012–2017) and Diputación Foral de Bizkaia Exp. 6-12-TK-2010-0027, Project SICURB-ITS- Desarrollo de Sistemas para el análisis de la Contaminación atmosférica en zonas URBanas integrados en ITS (2010–2011)

Promoting Public Health and Safety: A Predictive Modeling Software Analysis on Perceived Road Fatality Contributory Factors

Extensive literature search was conducted to computationally analyze the relationship between key perceived road fatality factors and public health impacts, in terms of mortality and morbidity. Heterogeneous sources of data on road fatality 1970-2005 and that based on interview questionnaire on European road drivers’ perception were sourced. Computational analysis was performed on these data using the Multilayer Perceptron model within the dtreg predictive modeling software. Driver factors had the highest relative significance. Drivers played significant role as causative agents of road accidents. A good degree of correlation was also observed when compared with results obtained by previous researchers. Sweden, UK, Finland, Denmark, Germany, France, Netherlands, and Austria, where road safety targets were set and EU targets adopted, experienced a faster and sharper reduction of road fatalities. However, Belgium, Ireland, Italy, Greece and Portugal experienced slow, but little reduction in cases of road fatalities. Spain experienced an increase in road fatalities possibly due to road fatalities enhancing factors. Estonia, Slovenia, Cyprus, Hungry, Czech Republic, Slovakia and Poland experienced a fluctuating but decreasing trend. Enforcement of road safety principles and regulations are needed to decrease the incidences of fatal accidents. Adoption of the EU target of -50% reductions of fatalities in all countries will help promote public health and safety